Template-Type: ReDIF-Article 1.0 Author-Name: Renjie Liu Author-X-Name-First: Renjie Author-X-Name-Last: Liu Author-Name: Zhiping Cheng Author-X-Name-First: Zhiping Author-X-Name-Last: Cheng Author-Name: Haotian Wu Author-X-Name-First: Haotian Author-X-Name-Last: Wu Title: Condition monitoring and fault warning of a ground network of hydropower station based on power internet of things Abstract: This study proposed the Power Internet of Things and Deep Learning-assisted Condition Monitoring and Fault Detection Model (PIoT-DL-CMFD) for effectively monitoring faults in the ground network of hydropower stations. Data segmentation is used first to reconstruct the raw vibration data, which may enhance training efficiency. Secondly, Long-Short-Term Memory (LSTM) can train the reconstruction data efficiently and adaptively under diverse operational conditions and fault factors. LSTM may then use network inference to detect the information fault classifications. Using the IoT, users can monitor storage conditions and control the devices by sending commands from any place in the world. The numerical findings illustrate that the recommended PIoT-DL-CMFD model enhances the fault prediction rate of 96.8%, accuracy ratio of 98.5%, overall performance ratio of 95.6%, water flow monitoring ratio of 94.5% and energy generation ratio of 97.2% compared to other popular methods. Journal: Int. J. of Global Energy Issues Pages: 69-90 Issue: 1/2 Volume: 48 Year: 2026 Keywords: hydropower station; deep learning; condition monitoring; fault detection; power internet of things; monitoring and control system. File-URL: http://www.inderscience.com/link.php?id=150711 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:69-90 Template-Type: ReDIF-Article 1.0 Author-Name: Chengxiang Yao Author-X-Name-First: Chengxiang Author-X-Name-Last: Yao Author-Name: Haijian Li Author-X-Name-First: Haijian Author-X-Name-Last: Li Author-Name: Junhan Li Author-X-Name-First: Junhan Author-X-Name-Last: Li Author-Name: Yang Li Author-X-Name-First: Yang Author-X-Name-Last: Li Author-Name: Yunjie Cai Author-X-Name-First: Yunjie Author-X-Name-Last: Cai Title: Exploration and practice of energy efficiency under natural gas well drainage and gas production process method Abstract: Natural gas, as a key fossil fuel, plays a vital role in the energy industry. This paper explores three drainage and gas production technologies - foam drainage, beam pumping unit deep well pump drainage, and gas lift drainage - with a focus on improving energy efficiency. By analysing experimental data, the study compares their performance in terms of cost and efficiency. At a drainage depth of 200 m, foam drainage was found to be the most cost-effective, reducing gas production costs by 170.02 yuan and 183.9 yuan compared to the other two methods. While energy efficiency among the three methods showed little difference, foam drainage is recommended for its lower cost, contributing to energy savings and technological advancement in natural gas production. Journal: Int. J. of Global Energy Issues Pages: 48-68 Issue: 1/2 Volume: 48 Year: 2026 Keywords: natural gas wells; drainage gas recovery; energy efficiency; deep well pump; foam drainage gas production process. File-URL: http://www.inderscience.com/link.php?id=150712 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:48-68 Template-Type: ReDIF-Article 1.0 Author-Name: Xingting Liu Author-X-Name-First: Xingting Author-X-Name-Last: Liu Author-Name: Ke Ning Author-X-Name-First: Ke Author-X-Name-Last: Ning Author-Name: Bin Hou Author-X-Name-First: Bin Author-X-Name-Last: Hou Author-Name: Jin Zhang Author-X-Name-First: Jin Author-X-Name-Last: Zhang Author-Name: Shanshan Gao Author-X-Name-First: Shanshan Author-X-Name-Last: Gao Author-Name: Jing Ma Author-X-Name-First: Jing Author-X-Name-Last: Ma Title: Intelligent distribution equipment status monitoring and fault warning based on the power internet of things Abstract: This paper used the power Internet of Things (IoT) technology to monitor the status of the intelligent DR and give early warning of faults. First, the existing problems of the current DR were introduced. This paper then analysed the status monitoring requirements and system configuration of the DR and then discussed the monitoring technology of the intelligent DR to deal with the corresponding data faults. At the end of this paper, the effect of condition monitoring and fault early warning of intelligent DR was analysed, and finally, the conclusion was drawn. After adopting the IoT, the monitoring accuracy of each intelligent DR has improved compared with the previous one. The timeliness of fault early warning in DR has been greatly improved after the adoption of power distribution network technology, and the timeliness of fault early warning in DR 5 can reach 92%. Journal: Int. J. of Global Energy Issues Pages: 3-23 Issue: 1/2 Volume: 48 Year: 2026 Keywords: intelligent distribution room; condition monitoring; fault warning; power IoT; monitoring accuracy. File-URL: http://www.inderscience.com/link.php?id=150714 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:3-23 Template-Type: ReDIF-Article 1.0 Author-Name: Ke Ning Author-X-Name-First: Ke Author-X-Name-Last: Ning Author-Name: Yang Bai Author-X-Name-First: Yang Author-X-Name-Last: Bai Author-Name: Bin Hou Author-X-Name-First: Bin Author-X-Name-Last: Hou Author-Name: Jin Zhang Author-X-Name-First: Jin Author-X-Name-Last: Zhang Author-Name: Shanshan Gao Author-X-Name-First: Shanshan Author-X-Name-Last: Gao Author-Name: Xingting Liu Author-X-Name-First: Xingting Author-X-Name-Last: Liu Title: Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing Abstract: This paper optimised the power grid equipment fault prediction model based on ML and high-performance computing, analysed the application of high-performance computers in online fault prediction and designed the overall structure of the mechanical equipment fault prediction and detection model. It explains the data classification and prediction in ML, describes how to establish prediction models and applies different ML algorithms to power grid equipment fault prediction models. Through experiments, comparing the optimisation effects of varying ML algorithms on power grid equipment fault prediction models, it was found that the Least Squares Support Vector Machine (LS-SVM) prediction algorithm has the highest accuracy and the best optimisation effect on power grid equipment fault prediction models. After using the LS-SVM prediction algorithm, the entire fault prediction time has been shortened. Journal: Int. J. of Global Energy Issues Pages: 116-137 Issue: 1/2 Volume: 48 Year: 2026 Keywords: predictive model; power grid equipment failure; high-performance computing; machine learning; LS-SVM prediction algorithm. File-URL: http://www.inderscience.com/link.php?id=150715 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:116-137 Template-Type: ReDIF-Article 1.0 Author-Name: Jing Wang Author-X-Name-First: Jing Author-X-Name-Last: Wang Author-Name: Qiong Wang Author-X-Name-First: Qiong Author-X-Name-Last: Wang Author-Name: Fangjun Li Author-X-Name-First: Fangjun Author-X-Name-Last: Li Author-Name: Zhenfen Zhang Author-X-Name-First: Zhenfen Author-X-Name-Last: Zhang Author-Name: Jianyong Gao Author-X-Name-First: Jianyong Author-X-Name-Last: Gao Title: Dynamic response analysis and control of power systems by combining differential equation models with power data Abstract: In response to the complex situation where the accuracy of describing the dynamic behaviour of the power system (PS) is low and the control strategy is difficult to cope with dynamic changes, this article combines differential equation models and power data to study the dynamic response analysis and control of the PS. Firstly, electricity data was collected from a certain power company, and the data was cleaned and standardised. Then, differential equation models were constructed for the generators, loads, and transmission lines in the PS, describing their dynamic behaviour and discretising the model. The MPC (Model Predictive Control) algorithm was used to define the objective function, set constraints, and solve the problem. The combination of differential equation modelling and MPC algorithm has improved the accuracy of describing the dynamic behaviour of the PS, and has good adaptability to complex dynamic changes, ensuring the safe and stable operation of the PS. Journal: Int. J. of Global Energy Issues Pages: 24-47 Issue: 1/2 Volume: 48 Year: 2026 Keywords: power system; power data; differential equation model; MPC algorithm; dynamic response analysis and control; description accuracy. File-URL: http://www.inderscience.com/link.php?id=150716 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:24-47 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaomeng Liu Author-X-Name-First: Xiaomeng Author-X-Name-Last: Liu Author-Name: Zuohu Chen Author-X-Name-First: Zuohu Author-X-Name-Last: Chen Author-Name: Wenlei Shi Author-X-Name-First: Wenlei Author-X-Name-Last: Shi Author-Name: Zhenguo Peng Author-X-Name-First: Zhenguo Author-X-Name-Last: Peng Author-Name: Wenxia Li Author-X-Name-First: Wenxia Author-X-Name-Last: Li Title: Differential equation modelling in dynamic modelling and prediction of power index data Abstract: This paper combined differential equation models to study the dynamic modelling and prediction of power index data. Firstly, it collected electricity data from a certain power company from 1 June to 15 June 2023, and selected electricity index data to construct a partial differential equation model. The boundary and initial conditions were defined, and nonlinear effects and coupling relationships were established. Then, the least squares method and gradient descent algorithm were combined to estimate the parameters of the partial differential equation model. It then solved the model using the fourth-order Runge-Kutta method. Finally, based on the results of differential equations, this paper introduced the LSTM model for the dynamic prediction of power indicators. It demonstrates that the combined differentiable equation model-LSTM method performs well in power indicator prediction, enhances prediction accuracy, and guarantees steady power system operation. It also demonstrates a good capacity for capturing data changes in indicators. Journal: Int. J. of Global Energy Issues Pages: 91-115 Issue: 1/2 Volume: 48 Year: 2026 Keywords: power system; power indicators; differential equation model; dynamic modelling; forecast accuracy; LSTM model. File-URL: http://www.inderscience.com/link.php?id=150717 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:91-115 Template-Type: ReDIF-Article 1.0 Author-Name: Jean Pierre Ndayiragije Author-X-Name-First: Jean Pierre Author-X-Name-Last: Ndayiragije Author-Name: Zaki Su'ud Author-X-Name-First: Zaki Author-X-Name-Last: Su'ud Author-Name: Abdul Waris Author-X-Name-First: Abdul Author-X-Name-Last: Waris Author-Name: Dwi Irwanto Author-X-Name-First: Dwi Author-X-Name-Last: Irwanto Title: Neutronic performance of natural uranium-thorium composite fuels on gas-cooled fast reactor employing modified CANDLE burn-up strategy Abstract: The modified CANDLE (Constant Axial shape of Neutron flux, nuclide densities and power shape During Life of Energy producing reactor) burn-up strategy was utilised effectively in both fast and thermal reactors. In this work, we investigated the neutronic performance of natural uranium-thorium (<SUP align=right><SMALL>238</SMALL></SUP>U-<SUP align=right><SMALL>232</SMALL></SUP>Th) composite fuels on a gas-cooled fast reactor employing a modified CANDLE burn-up shuffling in the radial direction. The investigation was carried out on a reactor of 450MWt with natural uranium and natural thorium composite fuels as input for the fuel cycle. The core has been subdivided into ten distinct regions directed radially. Neutronic calculations were carried out using SRAC coding, while JENDL 4.0 was used as a nuclear data library. The various volume fractions of thorium have been mixed with natural uranium as fuel to obtain the impact of natural thorium on the performance of the gas-cooled fast reactor design. The increased volume fraction of thorium caused a decrease in the effective multiplication factor but didn't affect the burn-up level significantly. The active core height has been varied to investigate its impact on reactor performance. The discharge burn-up level for 165 cm active core height is about 345 MWd/ton HM, while 170 cm active core is about 336 MWd/ton HM. Journal: Int. J. of Global Energy Issues Pages: 155-172 Issue: 1/2 Volume: 48 Year: 2026 Keywords: natural uranium; natural thorium; modified CANDLE; burn-up level; effective multiplication factor. File-URL: http://www.inderscience.com/link.php?id=150722 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:155-172 Template-Type: ReDIF-Article 1.0 Author-Name: Tao Feng Author-X-Name-First: Tao Author-X-Name-Last: Feng Author-Name: Chaodong Wang Author-X-Name-First: Chaodong Author-X-Name-Last: Wang Author-Name: Zhishuai Zheng Author-X-Name-First: Zhishuai Author-X-Name-Last: Zheng Title: An artificial intelligence wind power equipment operation and maintenance system and method using big data Abstract: With the continuous exploitation of fossil fuels, the reserves of this non-renewable energy source are increasingly being consumed. To alleviate the energy crisis and the environmental problems caused by fossil energy, wind power is now forming a boom in the world. However, the distribution of wind turbine units is relatively scattered, with each unit being far apart. Moreover, the cabins of wind turbines are mostly located at a height of several tens of metres, making traditional manual maintenance very difficult. The Artificial Intelligence (AI) wind power equipment Operation and Maintenance (O%M) system can display various technical indicators of the operation of each generator unit in real-time through Big Data (BD) technology, which plays an essential and positive role in reducing the O%M risks of O%M personnel and improving O%M efficiency. This article studied the O%M system and methods of wind power equipment using BD AI technology. The final experimental results showed that the wind power equipment O%M system using BD AI technology had an average maintenance difficulty score of 95.817 points, average maintenance duration of 4.208 hours and an average maintenance cost of 146,300 US dollars, which had significant advantages compared to traditional manual maintenance. Journal: Int. J. of Global Energy Issues Pages: 138-154 Issue: 1/2 Volume: 48 Year: 2026 Keywords: big data; artificial intelligence; wind power generation; operation and maintenance systems. File-URL: http://www.inderscience.com/link.php?id=150723 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:138-154 Template-Type: ReDIF-Article 1.0 Author-Name: Wenqi Ye Author-X-Name-First: Wenqi Author-X-Name-Last: Ye Author-Name: Xingken Liu Author-X-Name-First: Xingken Author-X-Name-Last: Liu Author-Name: Zhenbo Zhang Author-X-Name-First: Zhenbo Author-X-Name-Last: Zhang Title: Analysis of the demand for fossil fuels such as oil and coal and low-carbon emissions based on the computable general equilibrium model Abstract: This paper introduced an environmental module for measuring carbon emissions and carbon tax strategies and a dynamic model to the Computable General Equilibrium (CGE) model to transform it into a dynamic model, which is capable of simulating and analysing various low-carbon emission reduction strategies. A case study was then conducted. A social accounting matrix was constructed. Subsequently, the carbon tax and subsidy strategies were dynamically analysed. The scenarios of increasing the carbon tax rate and subsidy were compared with the baseline scenario. The results indicated that a low-carbon emission reduction strategy that increases the carbon tax could effectively reduce oil and coal consumption and carbon emissions. As the carbon tax rate increased, both oil and coal consumption and carbon emissions decreased. Moreover, implementing a carbon tax subsidy could mitigate the negative impacts on economic development caused by the carbon tax. Journal: Int. J. of Global Energy Issues Pages: 173-184 Issue: 1/2 Volume: 48 Year: 2026 Keywords: energy saving and emission reduction; carbon emission; taxation; accounting model. File-URL: http://www.inderscience.com/link.php?id=150724 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:48:y:2026:i:1/2:p:173-184 Template-Type: ReDIF-Article 1.0 Author-Name: Hefeng Song Author-X-Name-First: Hefeng Author-X-Name-Last: Song Author-Name: Xue Wang Author-X-Name-First: Xue Author-X-Name-Last: Wang Author-Name: Junhui Zhao Author-X-Name-First: Junhui Author-X-Name-Last: Zhao Author-Name: Shuai Yuan Author-X-Name-First: Shuai Author-X-Name-Last: Yuan Author-Name: Jiayi Yu Author-X-Name-First: Jiayi Author-X-Name-Last: Yu Title: Marine ecological governance and green development in Beibu Gulf of Guangxi under the digital context Abstract: With the rapid development of science, technology, and the economy, the Earth's ecosystem has suffered severe damage. The Guangxi Beibu Gulf Marine Region (GBGMR) is China's extremely important ecological barrier. This work aims to help formulate scientific and effective governance of the GBGMR and achieve Common Prosperity of the GBGMR to enable the high-quality and sustainable development of the GBGMR. This is achieved by constructing an integrated Ecological Carrying Capacity (ECC) model and introducing footprint breadth and depth analysis. The results show that the carbon and water ecological environments in the GBGMR show significant temporal and spatial differences. The ecological sustainability of Ningxia province along the GBGMR is the worst, while that of Henan is the strongest. Journal: Int. J. of Global Energy Issues Pages: 1-20 Issue: 7 Volume: 48 Year: 2026 Keywords: carbon footprint; water footprint; Guangxi Beibu Gulf Marine Region; ecological governance; traditional regression analysis; organisational psychology. File-URL: http://www.inderscience.com/link.php?id=152134 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijgeni:v:48:y:2026:i:7:p:1-20 Template-Type: ReDIF-Article 1.0 Author-Name: Meiling Dai Author-X-Name-First: Meiling Author-X-Name-Last: Dai Author-Name: Yuxin Ding Author-X-Name-First: Yuxin Author-X-Name-Last: Ding Author-Name: Peibin Zhu Author-X-Name-First: Peibin Author-X-Name-Last: Zhu Author-Name: Lingxiao Xu Author-X-Name-First: Lingxiao Author-X-Name-Last: Xu Title: Effect of international new energy teaching on promoting regional new energy communication based on intelligent BP algorithm Abstract: This study focuses on the application research of the intelligent Backpropagation (BP) algorithm in promoting regional new energy dissemination within international new energy teaching, exploring the practical value and mechanism of the algorithm from multiple dimensions. Based on the dataset of the Chinese Bridge Chinese Proficiency Competition for Foreign College Students and the learning data from the Chinese International Education Online platform, the study selects ten core features as input variables. They include learners' regional new energy cognitive basis, learning behaviour characteristics, and regional energy demand matching degree, while taking regional new energy dissemination effectiveness (covering knowledge mastery, dissemination willingness, and cooperative attitude) as the output variable to construct a BP neural network model. The research results enrich the theoretical system of international new energy education, and offer empirical support and practical guidance for designing regionally adaptive teaching programs and promoting the collaborative development of cross-border new energy technologies. Journal: Int. J. of Global Energy Issues Pages: 41-63 Issue: 7 Volume: 48 Year: 2026 Keywords: new energy learning; BP algorithm; international new energy; new energy teaching; regional new energy communication. File-URL: http://www.inderscience.com/link.php?id=152144 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijgeni:v:48:y:2026:i:7:p:41-63 Template-Type: ReDIF-Article 1.0 Author-Name: Jing Li Author-X-Name-First: Jing Author-X-Name-Last: Li Author-Name: Bo Wang Author-X-Name-First: Bo Author-X-Name-Last: Wang Author-Name: Tao Song Author-X-Name-First: Tao Author-X-Name-Last: Song Title: A digital technology for energy-saving ventilation control in underground infrastructures: integrating coupled simulation and BP algorithm Abstract: To address the challenges of energy inefficiency and delayed response in ventilation management of energy-intensive underground infrastructures, this study proposes a novel digital technology named Spatial Temporal Attention-based Back Propagation (STABP), which integrates coupled simulation and BP algorithm. It aims to achieve accurate prediction and dynamic regulation of ventilation demand in underground spaces, thereby optimising energy use. This study constructs a cross-disciplinary technological framework that integrates physical process simulation with data-driven algorithms, representing a novel approach to managing technological complexity in building energy systems. Firstly, based on Kriging interpolation method, spatial reconstruction is performed on limited sensor data to generate high-resolution gridded pollutant concentration fields, which serve as input boundary conditions for coupled simulation. Then, the BP algorithm is used for rapid dimensionality reduction and error compensation of high-dimensional spatiotemporal fields. Time series data is processed using Long Short-Term Memory (LSTM). Journal: Int. J. of Global Energy Issues Pages: 21-40 Issue: 7 Volume: 48 Year: 2026 Keywords: energy-intensive underground infrastructures; energy-saving ventilation control; coupled simulation; BP algorithm; spatio-temporal attention; LSTM; technology progress; digital energy; entrepreneurial strategy. File-URL: http://www.inderscience.com/link.php?id=152145 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijgeni:v:48:y:2026:i:7:p:21-40 Template-Type: ReDIF-Article 1.0 Author-Name: Mengyi Li Author-X-Name-First: Mengyi Author-X-Name-Last: Li Author-Name: Lin Wang Author-X-Name-First: Lin Author-X-Name-Last: Wang Title: Carbon comfort prediction and innovation enhancement for campus building clusters based on k-means clustering Abstract: Combined with the bidirectional long short-term memory network, a temporal prediction model is constructed to characterise the dynamic evolution characteristics of carbon emissions and environmental comfort. On this basis, a multi-objective optimisation framework is established. The non-dominated sorting genetic algorithm II is adopted to solve the optimal Pareto frontier, thus realising the coordinated trade-off and dynamic regulation of energy consumption and comfort. On the premise of maintaining the indoor thermal-humidity environment within the optimal comfort range, the energy consumption of lighting and Heating, Ventilation, and Air Conditioning (HVAC) systems is successfully reduced by 21.4%. The optimisation of environmental quality significantly improves the cognitive status of researchers, with an estimated 11.5% increase in innovative work efficiency. The research findings confirm that reducing the carbon footprint of campuses can effectively empower scientific research and innovative productivity, providing a scientific paradigm for the refined management of green and smart parks. Journal: Int. J. of Global Energy Issues Pages: 85-108 Issue: 7 Volume: 48 Year: 2026 Keywords: improved k-means clustering; carbon comfort; bidirectional long short-term memory; multi-objective collaborative optimisation; innovative productivity; smart energy management. File-URL: http://www.inderscience.com/link.php?id=152146 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijgeni:v:48:y:2026:i:7:p:85-108 Template-Type: ReDIF-Article 1.0 Author-Name: Yiyu Dai Author-X-Name-First: Yiyu Author-X-Name-Last: Dai Author-Name: Junzheng Lu Author-X-Name-First: Junzheng Author-X-Name-Last: Lu Author-Name: Zesen Li Author-X-Name-First: Zesen Author-X-Name-Last: Li Author-Name: Jiawei Li Author-X-Name-First: Jiawei Author-X-Name-Last: Li Author-Name: Mobina Rafieipour Author-X-Name-First: Mobina Author-X-Name-Last: Rafieipour Title: Network security threat identification based on GNN-transformer fusion model in energy cyber systems Abstract: At present, energy network security threat identification still faces the problem that temporal and network relationships are difficult to fuse. To address this issue, this study proposes a fusion model using Graph Neural Network (GNN) and Transformer model. This model mainly includes the following parts: using Graph Attention Network (GAN) to mine the spatial relationships between energy nodes and control entities; and using Multi-Head Self-Attention (MHSA) to extract long-range time series of energy regulation data. By combining the above two methods, the model well completes end-to-end threat detection for energy communication networks. The above research results verify that the method of joint modelling of spatial and temporal information has certain effectiveness in the field of energy network security, which provides a new idea for constructing adaptive threat identification methods in localised energy regulation networks. Journal: Int. J. of Global Energy Issues Pages: 64-84 Issue: 7 Volume: 48 Year: 2026 Keywords: energy network security threat identification; GNN-transformer; multi-head self-attention; spatio-temporal feature fusion; intrusion detection. File-URL: http://www.inderscience.com/link.php?id=152150 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijgeni:v:48:y:2026:i:7:p:64-84