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International Journal of Reasoning-based Intelligent Systems
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International Journal of Reasoning-based Intelligent Systems (64 papers in press) Regular Issues
Abstract: With the higher vocational English teaching evaluation from a single score-based assessment to process-oriented, the traditional method of relying on final exams and teachers experience for judgement has become difficult to fully reflect the effectiveness of students English learning. This paper takes students from a higher vocational college as the research object and designs questionnaires around dimensions. A total of 297 valid questionnaires were collected and a gradient boosting tree model was constructed based on variable assignment and standardisation processing to conduct quantitative evaluation of higher vocational students English learning effect. The research results show that the questionnaire data can be well transformed into model analysis variables and the gradient boosting tree algorithm effectively fits the comprehensive score of students English learning effect and identifies the importance of different learning factors. This paper provides data support for the diagnosis of higher vocational English learning, teaching intervention and formative evaluation optimisation. Keywords: gradient boosting tree algorithm; higher vocational education; English learning; evaluation. DOI: 10.1504/IJRIS.2026.10080480
Abstract: The proposed study proposes a pedagogical learning assessment fuzzy control (PLA-FC) framework for culturally adaptive piano education, which will focus on maintaining traditional cultural continuity amidst modern educational methods. The main innovation is the dual-variable fuzzy modelling of cultural adherence and instructional connectivity, scored together by a fuzzy inference system, quantifying cultural alignment at multiple learning levels. The experimental results are given to show that proposed music education framework is interpretable and scalable for culturally grounded music education and may be embedded into intelligent tutoring and online learning systems. The current validation is based on models, and further research will aim to extend this approach to real time learning, to multi-modal data integration and to empirical validation with outcomes from learner-centred learning. Keywords: cultural connectivity; fuzzy control; learning; piano pedagogy. DOI: 10.1504/IJRIS.2026.10080481
Abstract: This study constructs a multi-element pattern classification and generation model for art design teaching. A dataset of 20,000 images (10 types, 256 x 256 px) is used. The classification model (EfficientNet-B3 with attention) achieves 92.7% accuracy and 92.5% F1-score. The generation model (improved StyleGAN2 with feature matching loss, =10) achieves an FID of 29.4, lower than cGANs 48.6. A teaching assistant system was tested with 40 students. The experimental group showed significant improvements in creativity (3.52 4.38, p = 0.003) and pattern application proficiency (3.45 4.50, p<0.001). User evaluations rated creative novelty at 4.65, surpassing real patterns (4.30). System generation takes 1.65s (3.8s for 8 concurrent requests). Future work will introduce a diffusion model with 50 steps, targeting FID <25. Keywords: pattern classification; pattern generation; generating a countermeasure network; art design teaching. DOI: 10.1504/IJRIS.2026.10080603
Abstract: In piano teaching, audio and visual information are complementary, but single-modal methods are susceptible to frequency-doubling interference, occlusion, and time alignment errors. This study proposes an audiovisual piano transcription model (AV-PTM) with a frequency-domain sparse attention mechanism in the audio branch and an improved YOLOv8-Pose with large receptive field and loss ranking in the visual branch. An adaptive weight fusion mechanism enables cross-modal dynamic decision-making. On the OMAPS2 dataset, AV-PTM achieves F1 = 0.968, outperforming audio-only (0.941), visual-only (0.626), and CR-GCN by 7.92%. In occlusion, F1 = 0.939. Ablation confirms multimodal fusion improves stability. This system provides automatic analysis and feedback for intelligent piano instruction. Keywords: intelligent piano teaching; audiovisual integration; automatic piano transcription; YOLOv8-Pose; multimodal learning. DOI: 10.1504/IJRIS.2026.10080678
Abstract: Dance motion quality assessment is simultaneously affected by long-term temporal dependencies, subtle motion deviations, and the limited availability of high-quality annotated data. To address these challenges, this study proposed temporal attention and adversarial learning for dance motion (TAAL-DM). The model employed a three-dimensional convolutional neural network (3D-CNN) to extract local spatiotemporal features and utilised multi-head self-attention (MHSA) to capture long-range dependencies across video frames. In addition, a conditional wasserstein generative adversarial network with gradient penalty (CWGAN-GP) was introduced to constrain generated motion sequences toward the distribution of expert-standard dance movements. Experiments were conducted on three datasets, namely AIST++, FineDance, and ChinaDance-Eval. The results demonstrated that TAAL-DM achieved strong performance in scoring consistency, error control, and defect localisation. The model maintained stable performance when evaluated on unseen dancers and in cross-dance-style testing scenarios. However, its performance still declined Under conditions involving severe occlusion, highly complex poses, and unconstrained camera settings. Keywords: dance motion evaluation; sequential attention mechanism; adversarial learning; three-dimensional convolutional neural network; 3D-CNN; generative adversarial network; GAN; motion quality. DOI: 10.1504/IJRIS.2026.10080679
Abstract: This study is based on the graph volume product network to carry out related exploration, and puts forward a method that can systematically and quantitatively analyse the visual composition characteristics of picture books. First of all, data collection and pre-processing are carried out for 50 representative traditional culture picture books, and the main elements in the page are abstracted into nodes, and edges containing spatial and logical relations are established. Secondly, with the help of multi-layer GCN model, the node and page-level features are extracted, and the five quantitative indicators of balance, symmetry, visual focus, hierarchy and spatial density are calculated, so as to realise the operational analysis of composition features. This study not only provides a scientific quantitative tool for the visual narrative of picture books, but also provides an operable reference method for cultural education, picture book design and digital content analysis. Keywords: traditional culture; visual narration; graph volume product network; composition features; cultural elements. DOI: 10.1504/IJRIS.2026.10080745
Abstract: Traditional opera training relies on subjective master-apprentice instruction, absent of standardised evaluation. We propose an end-to-end intelligent platform integrating computer vision, generative AI and reinforcement learning for immersive adaptive training. The system contributes three core innovations: 1) a semantic-guided diffusion model generates dynamic 3D scenes from scripts; 2) a spatio-temporal graph convolutional network (ST-GCN) enables fine-grained motion assessment, achieving a keypoint detection error of 18.5 mm and a 93.4% motion scoring accuracy compared to expert evaluations; and 3) a proximal policy optimisation (PPO)-based adaptive strategy dynamically adjusts training difficulty, reducing average response latency to 50 ms. The platform demonstrates a 45% improvement in skill acquisition efficiency over traditional methods and maintains robust cross-genre generalisation (over 91% accuracy) with limited labelled samples. These results validate the platform's effectiveness for scalable, personalised opera training. Key limitations include dependency on high-end GPUs and reduced sensitivity to facial micro-expressions compared to human experts. Keywords: opera intelligent training; spatiotemporal graph convolutional network; reinforcement learning adaptive strategy; few-shot generalisation. DOI: 10.1504/IJRIS.2026.10080773
Abstract: In order to accurately predict the demand for skilled talents in regional industries and optimise the professional setting of adult education, this paper proposes a combined forecasting model based on LSTM-XGBoost, which uses the LSTM network to capture the long-term time-series dependence of skilled talents demand, and uses the integrated learning advantage of XGBoost model to strengthen the ability of nonlinear feature mining. The study uses skill talent demand data from 2023 to 2025 for six representative enterprises in Chinas eastern, central, and western regions as its research subject. Results reveal the combined model achieves lower MAE, RMSE and MAPE than single LSTM and XGBoost. It delivers minimal error fluctuations in five-fold time-series sliding-window cross-validation, yielding higher prediction accuracy. This hybrid model combines time-series memory and feature extraction strengths, offering a reliable method to forecast regional industrial skilled labour demand and dynamically adjust adult education schemes. Keywords: regional industry; skilled personnel; LSTM; XGBoost. DOI: 10.1504/IJRIS.2026.10080774
Abstract: Evaluating rural revitalisation performance requires addressing multidimensional indicators, regional heterogeneity, mixed direction indicators and fuzzy grade boundaries. This paper establishes a provincial evaluation framework integrating entropy weighting, fuzzy comprehensive evaluation, robustness checks, obstacle diagnosis, and machine learning assisted digital driver diagnosis; machine learning is limited to auxiliary diagnosis, excluding composite index construction and causal inference. Using 2023 cross-sectional data for 31 mainland Chinese provinces from the 2024 China Statistical Yearbook, Peking University Digital Financial Inclusion Index and provincial boundary datasets, our five dimensional, 12 metric system covers industrial prosperity, ecological livability, effective governance, public services and affluent living, with digital financial inclusion sub-indices as digital drivers. Shandong, Jiangsu and Zhejiang rank highest, while Qinghai, Tibet and Gansu lag. Agricultural machinery power and rural income dominate inter provincial disparities. Digitisation positively correlates with composite scores (Pearson r = 0.545). Robustness tests verify stable results, with random forest outputs for auxiliary diagnosis. This framework supports regional classification, constraint identification and digital empowerment assessment. Keywords: rural revitalisation; fuzzy comprehensive evaluation; entropy weighting; machine learning diagnostics; digital financial inclusion; DFI. DOI: 10.1504/IJRIS.2026.10080870
Abstract: Cavitation erosion of hydraulic pump impellers limits their service life and efficiency. Traditional iterative optimisation based on computational fluid dynamics takes 1.8 hours per iteration, making it difficult to achieve global optimisation. In this study, we developed a deep convolutional surrogate neural network that directly maps geometric parameters to the cavitation volume fraction field, reducing the evaluation time to 0.023 seconds with an average relative prediction error of 2.37%. Combined with a two-objective optimisation using genetic algorithms, experiments demonstrate that the optimized impeller reduces the required cavitation margin by 26.3% compared to the original design, while improving efficiency by 3.1 percentage points. Furthermore, the area under the receiver operating characteristic curve for cavitation state classification reaches 0.968. This deep neural network-driven surrogate optimisation paradigm resolves the conflict between high evaluation costs and global search, providing an engineerable, rapid iterative path for the design of cavitation-resistant hydraulic pump geometries. Keywords: deep neural networks; surrogate models; impeller geometry optimisation; cavitation; genetic algorithms. DOI: 10.1504/IJRIS.2026.10081076
Abstract: In this study, the video data set of basketball shooting, standing long jump, 50-meter running, solid ball throwing and sit-ups is constructed. VGG19 network migration learning strategy is adopted to freeze the first ten convolution layers, fine-tune the last six convolution layers and fully connected layers. The accuracy of the model on the test set is 90.8%, and the reasoning time is 42 milliseconds. After the system is deployed, the response time of action correction is reduced from 24 hours to 2.1 seconds, the accuracy of attendance statistics is increased to 99.7%, and the weekly management time of teachers is reduced from 10 hours to 2 hours. Ablation experiment showed that the pre-training weight contributed the most (increased by 8.3 percentage points). This work is an integrated framework that couples a VGG19-based action recognition engine with a physical education information management platform, operationalised through a quasi-experimental teaching intervention. Keywords: VGG19 network; physical education teaching; transfer learning; precise management. DOI: 10.1504/IJRIS.2026.10080871
Abstract: Analysing extensive corporate financial disclosures to identify latent risks represents a critical yet error-prone challenge for human auditors. As corporate structures grow increasingly intricate, overlooking pivotal financial warnings can precipitate catastrophic economic consequences for investors. Although automated systems offer potential solutions, conventional artificial intelligence models suffer from information overload and frequently generate hallucinatory numerical data. To overcome these limitations, we introduce a topological and rule-constrained audit network that integrates long-text semantic comprehension with structural corporate graphs. By strictly enforcing baseline accounting equations during textual synthesis, this framework effectively eliminates logical inconsistencies. Empirical evaluation demonstrates that our approach achieves a text generation score of 0.684, yielding a 14.38% improvement over baselines. Crucially, it suppresses the fact distortion rate to 2.1%, while attaining an area under the curve of 0.912 in predicting hidden default risks. Keywords: large language models; financial auditing; risk prediction; rule constraint; topological reasoning. DOI: 10.1504/IJRIS.2026.10080872 Abstract: Most EVT studies on Chinas equity market analyse a single index or a few futures contracts, leaving stock-level tail heterogeneity underexplored. This paper applies POT-GPD modelling to all 300 CSI 300 constituents using 248 daily loss observations per stock over a one-year window from 14 November 2024 to 21 November 2025. VaR and ES are estimated at the 95% and 99% levels under two thresholds and compared with historical simulation and Student-t benchmarks. Results show substantial heterogeneity in tail behaviour: 4756% of stocks have 1, making ES undefined, with this instability strongly linked to small-sample effects. Apparent cross-sectional variation in tail risk by sector and volatility is not statistically significant once within-group dispersion is accounted for, which reinforces the small-sample interpretation; energy stocks nonetheless show the most stable tails. Stress tests indicate large VaR amplification under deeper-tail and sigma-shock scenarios. Keywords: generalised Pareto distribution; value-at-risk; expected shortfall; maximum likelihood estimation; shape parameter; small-sample bias; bias correction; financial regulation; Fundamental Review of the Trading Book; FRTB; Chinese equity market; conditional extreme value theory; sector heterogeneity. DOI: 10.1504/IJRIS.2026.10081079
Abstract: Traditional athlete monitoring fails to capture multi-periodicity and long-term dependencies in physiological signals. We propose a TimesNet-LSTM fusion model for real-time fitness assessment and load regulation. Using 5-channel data from 20 runners, the model achieves superior accuracy (MAE: 0.041, RMSE: 0.058, R2: 0.96, MAPE: 4.2%). Ablation confirms the essential roles of both modules. The system triggers alerts at fitness score < 0.40 or heart rate > 92% of maximum, achieving 97% sensitivity with 12% false positives. Deployed with 78 ms edge latency, it offers a high-precision tool for endurance training. Keywords: TimesNet; long-term and short-term memory networks; physical fitness assessment of athletes; load regulation. DOI: 10.1504/IJRIS.2026.10081080
Abstract: To solve the problem that the graphic design works generated by artificial intelligence (AI) rely on subjective judgement and lack of standardised quantitative standards in aesthetic evaluation, this study proposes an aesthetic evaluation framework that integrates convolutional neural network (CNN) and expert scoring. Using CNN as the core model, the comparative experiment is carried out and compared with multi-layer perceptron, support vector machine, random forest and K nearest neighbour algorithm. The results show that CNN has the best prediction performance, with MSE of 0.042, MAE of 0.156 and R2 of 0.912, which is better than all the comparison models. The weight analysis shows that creativity and originality, colour coordination and aesthetic coherence are the most influential evaluation dimensions. The difference between CNN prediction score and expert score in all dimensions is less than 0.2, indicating that they are highly consistent. Keywords: convolutional neural network; CNN; expert scoring; artificial intelligence; graphic design; aesthetic evaluation system; analytic hierarchy process; AHP. DOI: 10.1504/IJRIS.2026.10081136
Abstract: Two key issues affect current English smart classrooms: disconnected oral pronunciation diagnosis and personalised learning-path recommendation, plus computational bottlenecks from long-duration teaching interaction data. To resolve these, this study proposes a coupled adaptive recommendation model (CARM) based on multimodal feature fusion. In this study, the closed-loop drive design of perception-coupling-decision is adopted: the front-end perception module uses convolution-augmented transformer (Conformer) to extract high-dimensional acoustic defect features. The experimental results show that the perceptual module achieves an accuracy of 85.55% in the pronunciation error detection task. The reasoning time of this model is only 142 ms when dealing with long sequences with the length of 2048, which is significantly better than the traditional Transformer model. The model achieved an area under curve (AUC) of 0.753 in adaptive recommendation tasks, verifying the effectiveness of its collaborative optimisation from accurate auditory diagnosis to dynamic path planning in complex teaching scenarios. Keywords: multimodal feature fusion; English wisdom classroom; informer; knowledge map; adaptive recommendation. DOI: 10.1504/IJRIS.2026.10081176 Face expression recognition for electricity marketing based on multiscale feature fusion with swin transformer by Yanan Cai, Jinghui Chen, Wei Ge Abstract: In this method, the proposed lightweight SPST module replaces the swin transformer blocks in the fourth stage of the original swin transformer model, significantly reducing the number of parameters and enabling lightweight and efficient inference. Subsequently, an EMA module is embedded after the second stage of the improved model to enhance the perception of subtle facial expression details through multi-scale feature extraction and cross-spatial information aggregation, thereby improving the accuracy and robustness of facial expression recognition in power marketing scenarios. Experimental results show that the proposed method achieves recognition accuracies of 97.56%, 86.46%, 87.29%, and 70.11% on the JAFFE, FERPLUS, RAF-DB, and FANE public facial expression datasets, respectively. Compared with the original swin transformer model, the improved model reduces the number of parameters by 15.8% and increases FPS by 9.6%, demonstrating significantly enhanced real-time performance while maintaining high recognition accuracy. Keywords: power marketing; face expression recognition; swin transformer; ST; multiscale feature fusion. DOI: 10.1504/IJRIS.2025.10073378 Research on a method for assessing the status of electric power metering assets based on neural network federated learning by Mingxin Jin, Shanshan Li, Guanna Lu, Yanguo Lv, Huinan Wang Abstract: This approach not only avoids the security risks associated with third-party coordination but also enhances the models performance in practical applications such as fault diagnosis and electricity bill recovery risk prediction. Additionally, an incentive mechanism based on multi-dimensional contribution assessment and a block chain-based smart contract implementation scheme is designed to provide a sustainable motivational guarantee for multi-party collaboration. Specifically, by exchanging encrypted intermediate parameters (such as gradients or weight updates) during model training, the method achieves effective integration and joint modelling of multi-party data values. Keywords: federated learning; information security; machine learning; neural networks; electricity metering. DOI: 10.1504/IJRIS.2025.10074388 Research on online error estimation method for station gate metering devices based on dynamic bus topology unit energy conservation by Qiang Song, Zhiyi Qu, Jing Yang, Qingqing Fu, Tiejun Cheng Abstract: This enables the establishment of a mapping between metering device errors and deviations in system energy conservation, forming a dynamic error modelling framework that reflects actual operating conditions. Then, a fading memory mechanism is introduced, and the FMRLS algorithm is employed to recursively estimate model parameters, thereby realising online and adaptive estimation of metering device errors. Simulation results demonstrate that, compared with the Levenberg-Marquardt (LM) algorithm and the limited memory recursive least squares (LMRLS) algorithm, the proposed method significantly improves the accuracy and dynamic responsiveness of error estimation while maintaining convergence stability. Keywords: error estimation; dynamic line loss; fading memory recursive least squares; online estimation. DOI: 10.1504/IJRIS.2025.10074598 Breast cancer classification refined using ResNet50 parameter tuning with lyre bird optimisation by Sabura Banu Urundai Meeran Abstract: Breast cancer remains a major cause of mortality among women, highlighting the need for accurate and efficient diagnostic methods. Deep learning, particularly CNNs, has improved medical image analysis, yet further optimisation is required for better precision and faster inference. This study optimises ResNet50 using the lyrebird optimisation (LBO) algorithm for hyperparameter tuning. A histopathological image dataset with cancer and non-cancer classes was used for training and evaluation. LBO fine-tuned key parameters such as learning rate, significantly enhancing model performance. The LBO-optimised ResNet50 outperformed standard ResNet50, Inception V3, and VGG16, achieving 98.85% accuracy along with high precision, recall, F1 score, and specificity (98.6%). The model also achieved an AUC-ROC of 99.98%, low log loss (0.0267), and reduced inference time (0.1377 seconds). Confusion matrix results showed fewer misclassifications. While promising for improving diagnostic reliability, additional clinical validation is recommended. Keywords: histopathological image analysis; deep learning models; hyperparameter tuning; diagnostic accuracy; medical image classification; confusion matrix analysis; performance optimisation; computer-aided diagnosis. DOI: 10.1504/IJRIS.2025.10075375 Research on error optimisation algorithm for station gate electric energy metering devices based on triplet Siamese networks by Qiang Song, Zhiyi Qu, Jing Yang, Qingqing Fu, Tiejun Cheng Abstract: The triplet Siamese network not only extracts features from the training samples themselves but also learns the similarities among samples of the same class and differences among samples of different classes, significantly enhancing the clustering effect and discriminative ability of the feature vectors. Simulation results demonstrate that the proposed algorithm achieves high accuracy and superior performance under small-sample conditions, significantly outperforming traditional machine learning methods and other deep learning models. It can effectively support error optimisation for station gate electric energy metering devices and contribute to enhancing the intelligence and security stability of power grid operations. Keywords: ternary twin network; Gram’s corner field; plant-station gateway; power metering device error. DOI: 10.1504/IJRIS.2025.10075376 Research on an online voltage unbalance mitigation method for distribution networks based on deep reinforcement learning by Lin Xu, Chang Liu, Houdong Xu, Yan Gong, Fuxin Li, Yi Zheng Abstract: Key innovations include: achieving model-free online decision-making, eliminating dependence on precise network parameters; possessing dynamic environment adaptability to respond in real-time to load fluctuations and distributed generation (DG) output variations; and simultaneously enhancing voltage balance and governance cost-effectiveness through reward function optimisation. Simulation results demonstrate that this method can effectively suppress voltage unbalance (reduced by over 30% in typical scenarios) within seconds, significantly decrease violation duration, and optimise compensation device switching frequency. It provides crucial technological support for constructing an intelligent and agile new-generation distribution network voltage governance system. Keywords: distribution grid; distributed resources; three-phase imbalance; intensive learning. DOI: 10.1504/IJRIS.2025.10075377 Research on data-driven methods for evaluating and predicting the health status of energy storage cell packs by Ning Li, Pengcheng Wei, Mingyang Wang, Yuan Liang, Dengyou Lei Abstract: To address the limitations of traditional scheduling methods in modelling multi-variable coupling relationships and dynamic response delays, this paper proposes an attention mechanism-based multi-layer neural network (AMNN) optimisation framework. By employing a bidirectional long short-term memory (bi-LSTM) network, a multidimensional time-series prediction model incorporating electricity price fluctuations, battery aging, and meteorological features is constructed to achieve precise perception of the energy storage system's operational status. Validation using real-world operational data demonstrates that compared to the PSO optimisation algorithm, this method reduces scheduling errors by 19.7% during sudden load fluctuations and lowers the lifetime cost per kilowatt-hour by 12.3%. Keywords: deep learning; electric energy metering; fault diagnosis; smart grid monitoring. DOI: 10.1504/IJRIS.2025.10075378 Simulation and dispatch optimisation of electricity spot markets considering renewable energy uncertainty by Xuanyuan Wang, Xu Gao, Zhen Ji, Wei Sun, Bo Yan, Bohao Sun Abstract: This paper aims to develop an integrated electricity spot market simulation and dispatch optimisation model that incorporates the characteristics of renewable energy. First, by establishing probabilistic models for wind and solar power output and employing stochastic programming or robust optimisation methods, the impact of their uncertainty on market clearing is characterised. Second, the objective function comprehensively considers factors such as minimising total system operating costs and maximising renewable energy integration, while the constraints rigorously account for grid security, unit technical limits, and power balance requirements, forming a complex mathematical optimisation problem. Keywords: simulation; distribution network; electricity spot markets; hierarchical planning. DOI: 10.1504/IJRIS.2026.10076057 Research on wide-area protection algorithms for power grids based on fault charge quantity comparison and distributed computing by Yu Sui, Xun Lu, Xiaoyu Deng, Wei Xu Abstract: The method employs fault charge comparison, where local integration is used to extract the positive and negative characteristics of fault currents on both sides, enabling distributed retention and transmission of key features. For switching information, it relies only on the stage II and III starting signals of distance protection, calculates the fault correlation coefficient combined with threshold criteria, and aggregates the basic probability assignments of adjacent lines, thereby reducing the need for centralised transmission across the network. The algorithm applies an improved evidence theory within the distributed framework to fuse multi-source information and reliably identify faulted lines. Keywords: fault charge quantity; wide-area protection; fault identification; fault tolerance; distributed computing. DOI: 10.1504/IJRIS.2026.10076502 Research on low-latency communication network methods for power system automation based on 5G technology by Yabin Chen, Wei Xu, Xiaoyu Deng, Yu Sui Abstract: This paper focuses on the stringent requirements for real-time performance, reliability, and security in communication transmission for power system automation, and researches low-latency communication network methods based on 5G technology. Traditional communication methods face challenges in meeting the low-latency and high-reliability demands of new services such as distributed intelligent control, wide-area protection, and precise load monitoring. It emphasises the in-depth integration of 5G technology with power automation services and the design of end-to-end communication solutions to support the future smart grid, thereby enhancing its rapid response and handling capabilities for renewable energy integration and complex faults. Keywords: 5G technology; power system automation; low latency; communication networks. DOI: 10.1504/IJRIS.2026.10076503 Research on modelling and anomaly analysis methods for metering errors at factory stations by Zhiyi Qu, Qiang Song, Pengcheng Li, Qinghui Chen, Tiejun Cheng Abstract: A norm-feedback algorithm is introduced to optimise the distribution of weights during training, thereby improving the convergence and predictive performance of the model. Based on the extracted error features, statistical methods are employed to derive confidence intervals for metering errors, enabling early identification and assessment of abnormal metering behaviours. Experimental results demonstrate that the method can accurately model the characteristics of gateway metering errors and identify multiple extreme conditions that may lead to significant deviations, providing theoretical support and technical means for enhancing the reliability and anomaly monitoring capability of substation gateway metering. Keywords: electricity metre; confidence interval of measured values; convolutional neural network: norm feedback algorithm. DOI: 10.1504/IJRIS.2026.10076504 Research on load dispatch and grid restoration in disaster-resilient emergency response for distribution networks based on nonlinear programming by Hao Dai, Guowei Liu, Lisheng Xin, Longlong Shang, Qingmiao Guo, Hao Deng Abstract: This paper addresses large-scale blackout scenarios in distribution networks following extreme disasters and investigates post-disaster emergency restoration strategies based on nonlinear programming. By constructing a multi-objective optimisation model that aims to maximise the total restored load and minimise switching operations, it comprehensively considers security constraints such as line capacity, voltage deviation, and radial operation, forming a mixed-integer nonlinear programming problem. The model effectively coordinates flexible resources like distributed generation and soft open points to achieve the coordinated optimisation of load transfer and network reconfiguration. Simulation results demonstrate that the proposed strategy significantly enhances the efficiency and resilience of post-disaster restoration. Keywords: deep learning; disaster-resilient; grid restoration; prediction model. DOI: 10.1504/IJRIS.2026.10076505 Research on source-load cooperative planning of distribution network based on carbon emission of distributed power sources by Hanyun Wang, Jianjie Jiang, Yang Liu, Tao Wang, Jiaqian Chen, Wei Zheng, Hai Liu Abstract: The paper proposes a source-load cooperative planning method for distribution networks based on load carbon emission characteristics. Based on the carbon potential and net load curve of the distribution network, a quantitative model of load carbon emission characteristics considering carbon emissions from electricity consumption, distance low carbon, trend low carbon and low carbon electricity consumption rate is established to obtain the low carbon planning priority of each distribution network within the regional grid. The carbon emission reduction benefits of the proposed method are analysed and verified by simulation on the improved IEEE30 node system. Keywords: carbon intensity; load carbon emission characteristics; low-carbon demand response; source-load synergy; low carbon planning. DOI: 10.1504/IJRIS.2026.10076684 Research on sparse prediction training technology for brain-like models based on pulse neural networks by Guoliang Zhang, Peng Zhang, Fei Zhou, Zexu Du, Jiangqi Chen, Zhisong Zhang, Qingyu Kong Abstract: This paper proposes a sparsity-prediction-based SNN training method, which introduces a predictive sparsity mechanism into the network structure to effectively reduce redundant computations and unnecessary synaptic updates. Specifically, during the training phase, an improved global recursive partitioning optimisation strategy is employed to enhance inter-cluster communication efficiency. Experimental results on five representative SNN benchmark models demonstrate that the proposed method significantly reduces communication latency and energy consumption while maintaining model accuracy, thereby improving training efficiency. Compared with existing approaches, it also exhibits clear advantages in terms of sparsity utilisation and energy efficiency. Keywords: pulse neural networks; brain-inspired processors; sparse prediction; training techniques. DOI: 10.1504/IJRIS.2026.10076685 Research on organisational forms and operational mechanisms of multimodal industry-education integration platforms by Na Xie Abstract: This study examines multimodal industry-education integration platforms, aiming to systematically analyse their diverse characteristics in organisational structure, constituent entities, and connection methods, along with their applicable scenarios. Through a combination of theoretical and practical analysis, this study aims to construct a more efficient and sustainable organisational and operational framework model for multimodal industry-education integration platforms. This framework seeks to effectively address the structural barriers and functional challenges encountered in the process of deepening industry-education integration, thereby supporting the enhancement of technical and skilled talent cultivation, strengthening industrial innovation momentum, and serving the high-quality development of regional economies. Keywords: multimodal; industry-education integration; organisational structure; operational mechanism. DOI: 10.1504/IJRIS.2026.10076686 A study on multitask deep learning-based prediction of student dropout risk and analysis of influencing factors by Guohua Sun, Hongxia Jia Abstract: To address the challenge of predicting student attrition risk in higher education institutions, this study proposes a multi-task deep learning-based early warning model for student dropout. This approach enables precise prediction of attrition risk and in-depth analysis of key influencing factors. By jointly learning multi-dimensional data including academic performance, behavioural characteristics, and personal attributes through a shared feature representation layer, the method simultaneously accomplishes attrition classification and factor analysis tasks. Experimental results demonstrate that this model achieves significant improvements in both prediction accuracy and stability compared to traditional single-task models. It effectively identifies key factors influencing student attrition, such as academic performance, attendance rates, and engagement levels, providing data-driven decision support for universities to implement targeted interventions and academic support. Keywords: multitask deep learning; student attrition; risk prediction; influencing factors. DOI: 10.1504/IJRIS.2026.10076691 Fermatean neutrosophic sets and their role in advanced decision-making systems by Prasanta Kumar Raut, K. Saritha, M. Gayathri Lakshmi, R. Rajalakshmi Abstract: In recent years, the need for effective representation and management of uncertain, imprecise, and inconsistent information has grown rapidly, especially in complex decision-making environments. Fermatean neutrosophic sets (FNS), a novel extension of neutrosophic sets, have emerged as a powerful mathematical tool capable of capturing higher degrees of uncertainty by relaxing conventional constraints. This paper presents a comprehensive overview of Fermatean neutrosophic sets, highlighting their foundational structure, key properties, and advantages over classical and intuitionistic fuzzy paradigms. Furthermore, we explore the pivotal role of FNS in advanced decision-making systems, including multi-criteria decision making (MCDM), risk assessment, and data classification problems. Illustrative examples and potential application domains are discussed to showcase the effectiveness of Fermatean neutrosophic models in real-world decision scenarios. Keywords: Fermatean neutrosophic set; FNS; uncertainty modelling; indeterminacy; neutrosophic logic; fuzzy systems. DOI: 10.1504/IJRIS.2026.10076734 Vibration analysis of Fe-based soft magnetic composite core reactor based on improved particle swarm algorithm by Yangyang Ma, Wenle Song, Jie Gao, Yang Liu, Yilei Shang, Weimei Zhao, Fuyao Yang Abstract: Using central composite design combined with finite element simulations, the study investigates the influence of different air-gap structural parameters on vibration responses and establishes an orthogonal polynomial-based response prediction model for accurately estimating core vibration displacement. Taking the minimisation of core vibration as the optimisation objective while maintaining the inductance value nearly constant, the optimal air-gap length of the reactor is obtained. The results show that under the optimised structural parameters, the maximum core vibration displacement is reduced by 10%, while the inductance variation is only 0.051%. This optimisation method provides significant reference value for reducing vibration and noise. Keywords: iron core reactor; electromagnetic-structural force field coupling; air gap structure; core vibration; orthogonal polynomial model. DOI: 10.1504/IJRIS.2026.10077021 An autonomous UAV trajectory optimisation and continuous stitching method for refined inspection of transmission lines by Lin Ao, Teng Ma Abstract: Combined with a spherical-threshold-based spatial density filtering method, redundant trajectory points near shooting locations are removed. Furthermore, a minimum turning radius constraint and arc smoothing are introduced to achieve trajectory smoothness, and flight safety validation is completed through safety distance constraints. Experimental results demonstrate that the proposed method can reduce the number of trajectory points for a single tower inspection by more than 90% while ensuring shooting consistency and flight safety. This significantly enhances the efficiency of UAV autonomous inspections and the reusability of trajectories, providing an engineering-feasible solution for refined and continuous autonomous inspections of transmission lines using UAVs. Keywords: autonomous inspection; trajectory optimisation; Douglas-Peucker algorithm; unmanned aerial vehicle; UAV. DOI: 10.1504/IJRIS.2026.10077022 Exploring the value and pathways of integrating algorithmic ethics education into ideological and political courses in the new era by Ge Chen, Xiaodong Yang Abstract: This paper explores the practical value and implementation pathways of integrating algorithm ethics education into ideological and political courses in the new era. Algorithmic technologies are profoundly transforming social life, and the ethical challenges they pose urgently require educational guidance to address. The study argues that integrating such education into ideological and political courses helps cultivate students correct understanding of technological ethics, sense of social responsibility, and value judgment capabilities, thereby achieving the unity of technological rationality and humanistic spirit. Specific pathways include: developing interdisciplinary teaching cases that integrate topics such as algorithmic transparency, fairness, and privacy with core socialist values; innovating teaching methods through scenario-based discussions and ethical deliberation; strengthening faculty training to enhance teachers technological ethics literacy; and establishing collaborative education mechanisms involving universities, enterprises, and societal stakeholders. Keywords: algorithmic ethics; educational integration; new era; ideological and political education courses. DOI: 10.1504/IJRIS.2026.10077113 Research on teaching and intelligent management based on multimodal fusion deep learning behaviour analysis by Wenjing Sun, Weisong Wang, Teng Ma Abstract: Addressing bottlenecks in traditional classroom teaching evaluations such as high subjectivity and delayed feedback this study explores intelligent teaching-management feedback mechanisms centred on multimodal fusion deep learning for behavioural analysis. By constructing an end-to-end intelligent analysis framework that integrates multi-source data including classroom visuals, audio, and text, and employing attention-based deep fusion strategies, it achieves fine-grained recognition and contextual understanding of teacher-student instructional behaviours. This research not only provides an innovative technical approach for classroom behaviour analysis but also drives a paradigm shift from experiential teaching management toward precision-driven, personalised educational governance through data-driven intelligent feedback mechanisms. Keywords: deep learning; instructional networks; teaching; classroom ecologisation; management system. DOI: 10.1504/IJRIS.2026.10077117 Research on deep learning-driven adaptive course resource recommendation and instructional planning by Jiaxue Liu, Xiaoxian Su Abstract: This study addresses the common challenges faced by online learning platforms, such as resource overload, rigid learning pathways, and lack of personalisation, by constructing an integrated framework for adaptive course resource recommendation and teaching planning based on deep learning. This framework combines a dual-channel learner dynamic perception model based on transformer and knowledge graph embedding, a resource representation approach that integrates knowledge structure and semantic information, and a long-term teaching planner based on deep reinforcement learning. Experiments on the public datasets ASSISTments2012 and EdNet indicate that our model improves recommendation accuracy by 12.5% compared to the best baseline methods, achieves a path rationality score of 4.5/5.0 as evaluated by experts, and enhances learning gains by 15%. The results suggest that the proposed framework effectively enables personalised cognitive navigation and teaching pathway planning, providing a feasible technical path for the development of the next generation of adaptive learning systems. Keywords: deep learning-driven; adaptive; course resource recommendation; teaching planning. DOI: 10.1504/IJRIS.2026.10077118 Research on the ecological management system of English teaching classrooms based on deep learning network technology by Jingshu Wu, Yawei Hu, Haodong Guo Abstract: With the rapid advancement of artificial intelligence technologies such as deep learning, traditional English teaching models are undergoing profound transformation. This study aims to establish an ecological management system for English classrooms. By constructing this ecological management model, the objectives are to achieve precise allocation of teaching resources, dynamic optimisation of teaching processes, intelligent recommendation of personalised learning paths, and diversified comprehensive teaching evaluation. This approach promotes the synergistic evolution and balanced development of all elements within the classroom ecosystem. Establish a theoretical framework and practical pathways for creating a new ecosystem of intelligent and harmonious English teaching. Keywords: deep learning; instructional networks; English teaching; classroom ecologisation; management system. DOI: 10.1504/IJRIS.2026.10077119 Research on the microstructure and magnetic properties of dual-phase composite magnetic materials by Jie Gao, Fuyao Yang, Yang Liu, Cong Wang, Pinpin Zhu, Zhibin Nie Abstract: This study systematically investigates the intrinsic relationship between microstructural characteristics and macroscopic magnetic properties in dual-phase composite magnetic materials. By adjusting preparation process parameters, composite microstructures with varying phase compositions, grain sizes, and interface morphologies were obtained. Combining microscopic analysis with magnetic measurements, the distribution, coupling state, and interface effects between soft and hard magnetic phases were analyzed in detail. Results indicate that exchange coupling between the two phases significantly influences the materials coercivity, remanence ratio, and maximum energy product. Optimising the microstructure effectively enhances magnetic properties, providing crucial theoretical and experimental foundations for designing and fabricating high-performance composite permanent magnet materials. Keywords: dual-phase composite; magnetic materials; microstructure; magnetisation; interphase interface. DOI: 10.1504/IJRIS.2026.10077121 Research on route planning methods based on an improved particle swarm optimisation algorithm by Yanfeng Xu, Yang Wang, Xiaobo Li, Xiang Xu Abstract: Addressing the need for intelligent optimisation of equipment layout and route schemes in complex geographical environments, this paper integrates geographic information systems (GIS) with intelligent optimisation algorithms to propose a route planning method based on an improved particle swarm optimisation (PSO) algorithm. The equipment arrangement and route planning problem is modelled as an optimisation model with multiple constraints. To solve this model efficiently, improvements are made to the standard PSO algorithm by introducing an adaptive inertia weight and a hybrid learning strategy, which effectively balance the algorithms global exploration and local exploitation capabilities, thus preventing premature convergence. Keywords: particle swarm optimisation; PSO; route planning; equipment scheduling; spatial analysis; intelligent algorithms. DOI: 10.1504/IJRIS.2026.10077122 A novel bipolar neutrosophic soft topological model for agricultural decision analysis by Prasanta Kumar Raut, R. Rajalakshmi, K. Saritha, M. Gayathri Lakshmi Abstract: Agricultural decision-making is frequently influenced by uncertainty stemming from environmental dynamics, soil heterogeneity, and varying agronomic conditions, which often lead to incomplete, vague, and even contradictory information. Conventional uncertainty modelling approaches, including fuzzy and intuitionistic frameworks, are not sufficiently equipped to address such complexity in a unified manner. In this paper, a novel analytical framework based on bipolar neutrosophic soft topological structures is proposed to effectively model these challenges. The introduced framework integrates the parameter-driven nature of soft sets with the capability of bipolar neutrosophic logic to simultaneously handle favourable and unfavourable information, together with the organisational advantages of topological structures. This combined model offers a refined representation of uncertainty and indeterminacy in decision environments. The applicability of the proposed methodology is demonstrated through multi-criteria decision-making problems in agriculture, including crop selection and resource management under uncertain scenarios. The results obtained from a detailed case study indicate that the proposed approach produces more robust, transparent, and reliable decisions compared to existing fuzzy-based techniques, highlighting its potential as a valuable tool for agricultural decision support systems. Keywords: bipolar neutrosophic set; BNS; soft topology; agricultural decision-making; uncertainty modelling; multi-criteria analysis; crop management. DOI: 10.1504/IJRIS.2026.10077123 Research on optimising the absorption capacity of feeders in medium and low-voltage distribution networks based on deep learning by Haitao Li, Xinming Xu, Xiaobin Chen Abstract: With the large-scale integration of distributed renewable energy, medium- and low voltage distribution networks face severe challenges regarding feeder hosting capacity. Traditional assessment methods rely on precise physical models and static scenarios, struggling to handle high-dimensional uncertainties and real-time optimisation needs. This paper proposes a deep learning-based method for optimising feeder hosting capacity, constructing a collaborative prediction-assessment-decision framework. Through a hierarchical-coordinated optimisation architecture (centralised upper-layer optimisation and distributed fast-response lower layer), simulation case studies verify that this method significantly enhances system hosting capacity (Power/psi index), smoothes power fluctuations at the point of common coupling, eliminates voltage limit violations, and improves three-phase unbalance and voltage fluctuation ratios. It provides an effective technical pathway for the safe and high-quality operation of distribution networks under high-penetration distributed energy integration. Keywords: absorption capacity; low-voltage; distribution networks; deep learning; DL. DOI: 10.1504/IJRIS.2026.10077602 Research on pre-trained transformer models incorporating domain knowledge in government big data analysis by Yunlong Sun Abstract: This paper explores the application of domain-knowledge-integrated pre-trained Transformer models in government big data analysis. Addressing the specialised nature and complex structure of government data, the study adapts general pre-trained models to specific domains and enhances their knowledge by incorporating domain dictionaries, entity knowledge, and business rules. It proposes training objectives and an integrated architecture tailored to the characteristics of government texts, thereby improving the models comprehension and reasoning capabilities in tasks such as policy analysis, public sentiment assessment, and service recommendation. Experiments demonstrate that this approach effectively improves the accuracy and interpretability of government text classification, information extraction, and decision support, providing robust technical support for smart government development. Keywords: domain knowledge integration; pre-training; transformer; government big data. DOI: 10.1504/IJRIS.2026.10077603 Research on resource aggregation scheduling and optimal control strategies for virtual power plants based on spatio-temporal data mining analysis by Hongtao Li, Dongyu He, Zhengzhe Li, Haoze Zhang, Liguo Zheng, Xingwang Jia, Xia Zhang Abstract: This paper addresses the resource allocation demands for flexibility in new power systems, focusing on the aggregation scheduling and optimisation control of virtual power plants (VPPs). It proposes a strategy framework based on spatio-temporal data mining and computational analysis. By mining the spatio-temporal correlation characteristics and operational patterns of distributed energy resources and loads, the study constructs a resource aggregation model that accounts for multiple uncertainties. Subsequently, a collaborative optimisation method integrating forecasting, scheduling, and feedback control is designed to achieve economically efficient scheduling and dynamic real-time control of virtual power plants across multiple time scales from day-ahead to intraday. Simulation results demonstrate that the proposed strategy effectively enhances the aggregation and control capabilities of virtual power plants over distributed resources, significantly improving system operational economics and renewable energy integration levels. This provides theoretical foundations and technical references for the engineering application of virtual power plants. Keywords: spatio-temporal data mining; computational analysis; virtual power plant; VPP; resource aggregation and dispatch. DOI: 10.1504/IJRIS.2026.10077604 Dynamic evolution analysis of relationships between traditional culture and Chinese literary figures based on knowledge graphs and graph neural networks by Li Cai, Qi Wang Abstract: This paper addresses the limitation in traditional culture and Chinese literary studies where character relationship analysis often remains static. It proposes an evolutionary analysis framework integrating dynamic knowledge graphs with graph neural networks (GNNs). By constructing a multimodal knowledge graph with temporal slices to encode characters, relationships, and their semantics, and combining GNNs with long short-term memory (LSTM) networks to capture network structure and temporal dependencies, this framework enables micro-level tracking and macro-level modelling of the entire process of character relationship formation, reinforcement, transformation, and rupture in classic works such as Dream of the Red Chamber and Romance of the Three Kingdoms. The research reveals that the evolution of literary character relationships exhibits a dynamic pattern intertwining event-driven abrupt changes with gradual shifts in emotional and ethical dynamics. This study provides a new semantic-driven computational pathway for digital humanities, advancing paradigm innovation in traditional cultural analysis. Keywords: knowledge graph; graph neural network; GNN; traditional culture; Chinese literary character relationships. DOI: 10.1504/IJRIS.2026.10077605 Research on intelligent accounting decision-making and financial education pathways based on deep reinforcement learning by Jia Feng, Xiaorui Xue Abstract: This paper addresses the limitations of traditional accounting decision-making, which relies heavily on experience and rules, and the lack of personalised pathways in financial education. It explores the application of deep reinforcement learning in the fields of intelligent accounting and financial education. The study constructs an intelligent accounting decision-making model based on deep reinforcement learning, optimising financial decisions through dynamic environmental interaction and reward mechanisms. Concurrently, an adaptive financial education path recommendation system is designed to dynamically adjust teaching content and difficulty levels in real-time based on learner behavioural data. Results demonstrate that this approach effectively enhances the precision and automation of accounting decisions while providing personalised, interactive learning solutions for financial education. It holds both theoretical and practical value for advancing the intelligent transformation of accounting and innovating financial talent cultivation models. Keywords: deep reinforcement learning; DRL; intelligent accounting decision-making; financial education; path analysis. DOI: 10.1504/IJRIS.2026.10077606 Research on uncertainty quantification algorithms based on Bayesian neural networks and a posteriori regularisation by Li Zhang Abstract: In security-sensitive applications of deep learning, accurately quantifying model prediction uncertainty is crucial. Bayesian neural networks, through their probabilistic framework, provide a theoretical foundation for simultaneously quantifying epistemic and aleatic uncertainty. However, traditional approximate inference methods (such as variational inference) often suffer from biased posterior distribution estimates due to approximation errors or inaccurate prior settings, leading to underestimated uncertainty or poor calibration. To address this, this paper proposes a novel Bayesian neural network uncertainty quantification method incorporating posterior regularisation. By introducing regularisation terms based on information theory or task-specific knowledge into the variational objective function, this approach constrains the shape of the posterior distribution, thereby guiding the model to learn more accurate and better calibrated uncertainty estimates. Keywords: Bayesian; neural network; a posteriori regularisation; uncertainty quantification. DOI: 10.1504/IJRIS.2026.10077733 Research on deep learning-based evaluation mechanisms for ideological and political education by Yaxin Li, Xiaojuan Zhang Abstract: As the process of educational informatisation continues to deepen, traditional ideological and political education evaluation mechanisms face challenges in terms of dynamism, precision, and scientific rigor. This study focuses on applying deep learning technology to innovatively construct an evaluation system for ideological and political education. It aims to achieve multidimensional, intelligent analysis of the learning process through methods such as neural networks and natural language processing. The study explores a hybrid evaluation model integrating student behavioural data, textual sentiment analysis, and cognitive feedback to quantitatively assess educational outcomes, dynamically track ideological development, and provide data support for personalised teaching interventions. Results indicate that deep learning assisted evaluation mechanisms effectively enhance objectivity, real-time responsiveness, and predictive capabilities, offering a viable technical pathway and practical reference for advancing the precision and scientific rigor of ideological and political education. Keywords: deep learning; supportive ideological and political education; educational evaluation mechanisms; political education. DOI: 10.1504/IJRIS.2026.10078009 Subset predicate encryption supporting a wholesaler by Kamalesh Acharya Abstract: Predicate encryption (PE) is a public key cryptographic primitive which gives fine-grained access structure in the cryptographic framework. Subset predicate encryption (SPE) is a variant of PE in which the decryption of the ciphertext corresponding to set S will be successful by using a secret key corresponding to set T if T S. In this work, we introduce subset predicate encryption supporting a wholesaler (SPeW), a new variant of SPE that incorporates an additional entity called as the wholesaler who purchases content in bulk and distributes it to a group of subscribers while preserving fine-grained cryptographic access control. Unlike previous SPE, SPeW provides a three-layered security guarantee: group privacy, a bound on group size, and security against illegal users. Our construction achieves adaptive security in the standard model while maintaining constant-size ciphertexts and secret keys, a property not achieved simultaneously in prior work. We implement SPeW using the PBC library and benchmark its performance across subscriber group sizes ranging from 1,200 to 1,800. The results show that setup and key generation, verification, and encryption times remain almost constant with group size, while decryption and group token generation scale linearly with group size. Keywords: subset predicate encryption; SPE; constant-size ciphertext; wholesaler; group privacy; bound on group size; security against illegal users. DOI: 10.1504/IJRIS.2026.10078084 Construction and application research of intelligent analysis model for course management data based on deep learning by Qian Ma, Yunqiao Peng Abstract: This study aims to construct a deep learning-based intelligent analysis model for course management data and explore its practical application value. The research first performs pre-processing and feature engineering on multi-source heterogeneous data within course management, establishing a high-quality data foundation for model training. Subsequently, tailored to the characteristics of educational data, a hybrid deep learning model integrating CNN and LSTM Networks is designed and constructed. This model effectively captures local features and long-term temporal dependencies within the data, enabling precise analysis for critical tasks such as academic performance early warning, learning behaviour pattern recognition, and course teaching quality evaluation. Keywords: deep learning; course management; data intelligence analysis; teaching management. DOI: 10.1504/IJRIS.2026.10078085 A method for detecting ablation in high-voltage cable buffer layers based on convolutional neural networks with waveform feature fusion by Kang Guo, Zhengping Wang, Zhibo Tian, Qian Li, Siying Wang, Zhenwei Yang, Dongmei Sun Abstract: To address the challenge of effectively identifying erosion defects in the aluminium sheath-buffer layer of high-voltage cables, this paper proposes an intelligent detection method based on waveform feature fusion convolutional neural networks (CNNs). By extracting multidimensional time-frequency domain features from oscillation wave test (OWTS) response signals, a fusion waveform feature vector is constructed. A one-dimensional convolutional neural network is designed to perform adaptive deep feature learning and state classification. Experimental results demonstrate that this method can accurately distinguish between normal, minor burn-through, and severe burn-through states, achieving a detection accuracy of 98.2%. This performance significantly outperforms traditional signal analysis methods, providing a reliable technical approach for the early diagnosis of buffer layer burn-through faults in high voltage cables. Keywords: waveform characteristics; convolutional neural networks; CNNs; power cables; buffer layer; ablation detection. DOI: 10.1504/IJRIS.2026.10078340 Artificial intelligence empowering ideological and political education: research on scenario applications, risk regulation, and ethical boundaries by Wen Jiang, Yan Lin Abstract: This paper focuses on innovative practices in AI-empowered ideological and political education, systematically exploring its application pathways in scenarios such as intelligent teaching, personalised tutoring, and public opinion analysis. The study indicates that AI technology significantly enhances educational effectiveness through precise content delivery and innovative interaction models, yet simultaneously faces multiple risks including algorithmic bias, data privacy concerns, and lack of emotional interaction. In response, the paper proposes establishing a risk regulation system encompassing technological oversight, ethical review, and legal safeguards. It further delineates the ethical boundaries of human-machine collaboration, emphasising the imperative to uphold educational autonomy, value-driven leadership, and humanistic warmth. This framework provides theoretical reference and practical guidance for the healthy development of ideological and political education in the AI era. Keywords: artificial intelligence empowerment; ideological and political education; scenario applications; risk regulation; ethical boundaries. DOI: 10.1504/IJRIS.2026.10078342 Multi-attribute decision-making and dynamic threshold-based sequencing of start-up power sources and stability control strategy for isolated microgrid start-up by Jinhao Shen, Hua Zhang, Xueneng Su, Yinwen Gao, Kun Zheng, Cheng Long, Hangqian Hou Abstract: This paper addresses the black start challenge in isolated microgrids by proposing a strategy that integrates multi-attribute decision-making with dynamic threshold adjustment for power source sequencing and stable control during the start-up process. First, a comprehensive evaluation index system for start-up power sources is established by considering multiple attributes such as power capacity, response speed, and regulation capability. Multi-attribute decision-making methods are then applied to rank candidate power sources. Building upon this foundation, a dynamic threshold control mechanism is designed to adaptively adjust critical operational parameter thresholds based on the microgrids real-time status and load restoration progress, achieving dynamic optimisation and active stability control during the startup process. Simulation results demonstrate that the proposed strategy can scientifically select the optimal startup power source sequence, effectively enhancing the stability and restoration efficiency of the microgrid startup process. Keywords: multi-attribute decision-making; dynamic threshold; isolated microgrid; start-up power source sequencing. DOI: 10.1504/IJRIS.2026.10078343 Distributed model predictive control method for resource clusters with communication delays and resource by Jinhao Shen, Hua Zhang, Xueneng Su, Yinwen Gao, Kun Zheng, Cheng Long, Hangqian Hou Abstract: To address the common challenges of communication delays and resource heterogeneity in distributed resource clusters, this paper proposes a novel distributed model predictive control method. By designing a collaborative predictive control framework with asynchronous update and delay compensation mechanisms, the proposed method effectively mitigates the impact of non-ideal communication between heterogeneous nodes. Simultaneously, an adaptive resource optimisation strategy is introduced, enabling each heterogeneous node to dynamically adjust the complexity of its local predictive model and control law based on its computational and storage capabilities. Theoretical analysis and simulation results demonstrate that this approach significantly enhances the control robustness and operational efficiency of heterogeneous clusters under delayed conditions, while ensuring system stability and optimised performance. Keywords: communication delay; resource heterogeneity; distributed resources; clustered distributed; predictive control. DOI: 10.1504/IJRIS.2026.10078344 AI+ journalism project driven teaching: integrated media content production practice incorporating neural by Qian Ma, Chune Shen Abstract: This paper explores a project-driven teaching model for AI+ journalism, focusing on the practical application of neural network algorithms in converged media content production. Guided by real-world projects, this model integrates intelligent technologies throughout the entire news workflow from gathering and writing to editing and distribution. By establishing a teaching framework that merges algorithmic models with journalistic practice, it cultivates students abilities to utilise AI tools for data mining, automated writing, and personalised content creation. Practice demonstrates that this interdisciplinary teaching approach not only enhances the efficiency and innovation of news production but also provides a practical pathway for cultivating journalism talent in the era of converged media. It holds significant promise for advancing the intelligent transformation of the journalism industry. Keywords: AI+ news; drive-based teaching; convolutional neural networks; converged media content. DOI: 10.1504/IJRIS.2026.10078345 Analysis based on recommendation algorithm logic and Marxist alienation by Weisong Wang, Wenjing Sun Abstract: This paper examines the inherent logic and societal impact of recommendation algorithms in the digital age from the perspective of Marxist alienation theory. The study reveals that through datafication, personalisation, and behavioural steering, recommendation algorithms gradually strip users of autonomy in information acquisition and value judgment. Users become trapped within platform-driven information silos and consumption cycles, intensifying multiple forms of alienation manifesting in labour, products, human essence, and interpersonal relationships. Through theoretical analysis and case studies, the paper reveals how algorithms, beneath their facade of efficiency enhancement, conceal a logic of subjectivity dissolution and capital control. It ultimately advocates for constructing an algorithmic governance pathway centred on the comprehensive development of humanity, thereby steering technological applications back to serving their social essence: the human being. Keywords: recommendation algorithms; logic; Marxism; alienation theory. DOI: 10.1504/IJRIS.2026.10078522 Exploration of teaching reform in innovation and entrepreneurship courses at higher education institutions based on knowledge graphs by Mingzhe Li, Jinghuan Zhu Abstract: This paper explores pathways and methodologies for applying knowledge graph technology to teaching reform in innovation and entrepreneurship courses at higher education institutions. It addresses practical challenges in such courses including insufficient industry-education integration, fragmented knowledge systems, and monolithic evaluation mechanisms while aligning with the trend toward educational digital transformation. The study proposes a knowledge graph-based teaching reform framework encompassing course knowledge system construction, personalised learning path recommendations, and dynamic teaching effectiveness assessment. This aims to systematise teaching content and refine learning processes. Finally, the paper summarises the practical value of knowledge graph-driven teaching model innovation, providing theoretical references and practical directions for enhancing the quality of innovation and entrepreneurship education. Keywords: higher education institutions; innovation and entrepreneurship courses; knowledge graphs; teaching reform; industry-education integration. DOI: 10.1504/IJRIS.2026.10078523 Research on real time 3D reconstruction method for distribution network channels using pseudo-stereo vision and SLAM-based UAVs by Jiaxing Fu, Xiangdong Zu, Changyong Huang, Chuang Yu, Hai Zhao Abstract: This paper investigates autonomous UAV flight and precise control technologies for vegetation encroachment inspection in distribution network corridors. Aiming at the complex corridor environment characterised by narrow spaces and multiple obstacles, this research focuses on achieving fully autonomous and high-precision inspection operations. The core contributions include: firstly, developing a multi-sensor data fusion perception method based on onboard vision and LiDAR to reconstruct high-precision 3D spatial positions and distributions of targets such as conductors, utility poles, and trees in real-time, thereby constructing local environment maps for autonomous navigation. Secondly, designing an online planning algorithm based on corridor geometric constraints and inspection objectives to dynamically compute optimal shooting positions, safe flight trajectories, and gimbal attitudes that comprehensively cover key vegetation encroachment areas while ensuring image quality and completeness. Keywords: drone inspection; visual SLAM; pseudo-stereoscopic 3D reconstruction; pseudo-stereoscopic vision system. DOI: 10.1504/IJRIS.2026.10078524 Research on generating and recommending personalised professional development pathways for teachers based on practice communities and instructional by Can Wang Abstract: This study focuses on constructing personalised professional development pathways for teachers by integrating communities of practice (CoP) with instructional behaviour data. Through systematic analysis of interactive discussions and experience sharing within CoPs, alongside routine instructional behaviour data (such as classroom recordings, student feedback, and assessment results), a multidimensional competency profile for teachers is established. Building upon this foundation, data mining and intelligent recommendation technologies are employed to generate and recommend personalised professional development pathways tailored to each teacher's developmental stage and practical needs. The research aims to overcome the limitations of traditional one-size-fits-all training models by providing teachers with precise, dynamic, and sustainable professional growth support. This approach seeks to enhance the effectiveness of teacher professional development while exploring theoretical and practical pathways for data-driven innovation in teacher development models. Keywords: practice communities; teaching behaviour data; teacher personalisation; professional development pathways. DOI: 10.1504/IJRIS.2026.10078619 Research on path identification and effect evaluation of deep integration between digital technology and the real economy by Huanxia Xue, Jing Ji Abstract: This paper focuses on key issues concerning the deep integration of digital technologies with the real economy, systematically exploring the pathways and comprehensive effects of such convergence. The study first identifies primary integration pathways, including technological empowerment, platform ecosystem construction, data-driven innovation, and organisational process restructuring. Building upon this foundation, a multidimensional evaluation framework is constructed to empirically analyse the significant positive effects of the integration process on industrial efficiency, innovation models, employment structures, and the quality of economic growth. Concurrently, potential challenges such as transformation risks and the digital divide are identified. Finally, the study proposes targeted policy and strategic recommendations aimed at providing theoretical foundations and practical references for promoting the organic integration of digital technology and the real economy, thereby fostering high-quality economic development. Keywords: digital technology; real economy; deep integration; path identification; effect evaluation. DOI: 10.1504/IJRIS.2026.10078946 Fileless malware attack detection based on multimodal large language models and verifiable reward reinforcement learning by Biao Liang, Yongming Chen, Wanling Zhao, Yongxing Lai, Mingjie Xu, Ge Jin Abstract: With the rapid advancement of smart grid construction and the integration of distributed energy resources, large-scale deployment of edge devices in power information systems is increasingly threatened by the surge of fileless attacks, creating an urgent need for lightweight detection techniques to ensure edge security. To address these challenges, we propose a lightweight, multimodal large language model framework for fileless attack detection, augmented with reinforcement learning guided by verifiable rewards. Memory dumps from active execution are converted into multimodal textual and visual representations for model training. Efficiency is improved via visual encoder compression, image patching, and attention pruning, while robustness is enhanced through group-relative policy optimisation. Extensive experiments on real-world datasets demonstrate that our approach achieves superior detection accuracy while maintaining practical efficiency, enabling real-time deployment on edge devices. Keywords: fileless malware attack detection; multimodal large language models; MMLMs; pruning; group-relative policy optimisation; reinforcement learning. DOI: 10.1504/IJRIS.2026.10079165 Research on an optimisation model for power grid companies electricity purchase and sale portfolio targeting multi-variety power transactions by Le Liu, Mingyu Che, Wenbo Yang, Wen Song, Ling Li, Xitong Ma, Jiafan Yang Abstract: In response to the increasing complexity of power purchase and sale decisions faced by grid companies amid the growing diversity of electricity market trading products, this paper constructs an optimisation model for power purchase and sale portfolio management tailored to multi-product electricity trading. The model comprehensively considers various transaction types, including medium- and long-term contract trading, spot market trading, and ancillary service trading. It aims to minimise the companys power purchase costs while maximising power sales revenue, incorporating constraints such as power balance, market pricing, and risk limitations to establish a stochastic optimisation model. By quantifying market volatility risk through the value at risk (VaR) metric and employing linear programming with scenario analysis for model solution, empirical research demonstrates that the proposed model effectively optimises power grid enterprises electricity purchase and sale portfolio strategies in multi market environments. It significantly enhances overall operational efficiency while controlling risk. Keywords: multi-product electricity trading; power grid companies; electricity purchase and sale; portfolio optimisation. DOI: 10.1504/IJRIS.2026.10079987 Research on hyper-personalised learning path generation algorithms tailored to cognitive characteristics in educational ecosystems by Qin Kong, Luhong Tang, Yihua Li Abstract: This paper investigates hyper-personalised learning path generation algorithms tailored to the cognitive characteristics of educational ecosystems. Addressing issues such as the homogeneity and lack of dynamic adaptability in traditional learning paths, it proposes a multidimensional modelling approach that integrates learner cognitive states, knowledge graphs, and behavioural data. By analysing individual cognitive differences and group interaction patterns within educational ecosystems, it constructs dynamic learner profiles and designs a hybrid algorithm based on reinforcement learning and sequence recommendation. This enables real-time personalised generation and optimisation of learning paths. Experiments demonstrate that this algorithm effectively enhances learning efficiency and knowledge mastery, providing theoretical support and technical pathways for precision teaching within smart education environments. Keywords: educational ecosystem; cognitive characteristics; hyper-personalised learning; path generation. DOI: 10.1504/IJRIS.2026.10080274 |
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