International Journal of Ad Hoc and Ubiquitous Computing (20 papers in press)

Regular Issues

• Open Access

  Intelligent Monitoring of Patient Vital Signs Based on Adaptive Attention Fusion Spatiotemporal Graph Neural Network ( Free Full-text Access )

  by Shunda Cheng, Jie Zhu, Shengjiang Guan, Jie Cheng, Tong Dou 
  Abstract: This study proposes a vital signs monitoring framework that addresses the limitations of traditional threshold-based alarms and existing deep-learning models in capturing multimodal physiological interactions and spatiotemporal dynamics. The method integrates an adaptive attention fusion mechanism that dynamically adjusts the importance of heterogeneous physiological parameters, a spatiotemporal graph neural network that jointly models inter-parameter correlations and temporal evolution using multi-scale windows, and a reinforcement learning module that enables active, strategy-driven early warning and clinical decision support. Evaluated on the MIMIC-III and eICU datasets, the proposed system achieves 96.3% anomaly detection accuracy, 38.5-minute early warning capability, and a 0.912 F1-score, outperforming existing methods. Ablation studies confirm the contributions of adaptive fusion, spatiotemporal graph modelling, and policy optimisation.
  Keywords: Adaptive attention mechanism; Spatiotemporal graph neural network; Vital sign monitoring; Deep reinforcement learning; Multimodal fusion.
  DOI: 10.1504/IJAHUC.2026.10076081
   
  

• Open Access

  Adaptive Deep Reinforcement Learning-Based Error Analysis Model for Strength Training ( Free Full-text Access )

  by Guobao Zhang, Haotian Li, Xurui Liu 
  Abstract: To tackle limitations of conventional strength training movement evaluation, such as inadequate movement stage segmentation and insufficient fine-grained error perception, this study proposes ARLE-Net. This framework integrates improved YOLOv8 with CBAM attention mechanism to enhance local movement feature detection, HR-Net for high-resolution human keypoint extraction, and adaptive reinforcement learning for dynamic action segmentation and standardized score prediction. Experiments on Fitness-AQA and ARFit datasets show it outperforms state-of-the-art methods in action segmentation accuracy and score prediction mean square error, with strong stability. Ablation studies confirm component effectiveness, providing support for intelligent fitness guidance and fitting the journal’s enhanced ubiquitous computing focus.
  Keywords: Strength Training; Pose Estimation; Reinforcement Learning; Action Quality Assessment.
  DOI: 10.1504/IJAHUC.2026.10076102
   
  

• Open Access

  Intelligent Automotive Trade Market Forecasting and Decision-Making System: Multi-Source Data Fusion under Meta-Learning ( Free Full-text Access )

  by Kan Lu, Mingting Huang, Min Song 
  Abstract: With the continuous advancement of intelligent vehicle technology, the smart automotive trade market has become increasingly complex and dynamic. This study proposes an intelligent forecasting framework that integrates multi-source data through a meta-learning-based approach. Specifically, a Meta-Attention Gated Recurrent Unit (MA-GRU) model is developed to enhance the accuracy and robustness of market predictions. The model first extracts key automotive performance indicators and market-related features using a GRU network, and then applies an attention mechanism to capture the most informative temporal dependencies. To address the challenge of data scarcity in the emerging smart vehicle market, meta-learning is introduced to improve the model's adaptability and generalisation across diverse datasets. Experimental evaluations demonstrate that the proposed MA-GRU framework achieves superior predictive performance even under limited data conditions, providing a solid technical foundation for market trend analysis and strategic decision-making in the intelligent automotive trade.
  Keywords: Intelligent marketing prediction; Intelligent cars; GRU; Meta-learning.
  DOI: 10.1504/IJAHUC.2026.10076104
   
  

• Open Access

  Integrating Multi-modal Emotion Recognition with Strategy Generation: A Transformer Approach to Sustainable Marketing ( Free Full-text Access )

  by Anqi Wu, Jing Wang, Jie Zhang, Wei Qiu 
  Abstract: This paper proposes the MEPSO-Transformer (Multi-modal Emotion Perception and Strategy Optimization Transformer), a dual-task architecture designed to jointly perform emotion classification and green marketing strategy recommendation based on heterogeneous user data. The model incorporates modality-specific encoders, hierarchical gated cross-attention fusion, and two parallel decoders, and is trained under a multi-task learning scheme. To evaluate its effectiveness, experiments are conducted on three datasets: CMU-MOSEI, MELD, and a constructed domain-specific dataset, GreenPromo-Emotion, which contains 6,200 multi-modal samples annotated with seven emotional categories and five predefined marketing strategies. Results show that MEPSO-Transformer achieves an average accuracy of 84.3% and a macro-F1 score of 81.2% on CMU-MOSEI, outperforming the best baseline by +2.3% and +2.6% respectively. On MELD, the model attains a 75.0% macro-F1 and a Hit@1 score of 65.7%. These findings demonstrate the model's applicability to sustainable marketing tasks requiring real-time affective understanding and strategy selection.
  Keywords: Multi-modal Emotion Recognition; Green Marketing; Strategy Optimization; Transformer; Sustainable AI; Affective Computing; Multi-task Learning.
  DOI: 10.1504/IJAHUC.2026.10076105
   
  

• Open Access

  Design and Optimisation of Multi-Party Secure Computing Protocols Supporting Dynamic Participant Switching ( Free Full-text Access )

  by Zhipeng Li, Haiping Zhao, Shaobo Liu, Hao Yang, Shuo Shen, Rui Dong, Yu Zhang 
  Abstract: Most existing SMPC protocols assume static participants, unsuitable for dynamic scenarios where participant numbers and identities change. This paper proposes an SMPC protocol enabling dynamic participant switching, introducing a dynamically joinable model with variable committee sizes per round and supporting arbitrary member counts. It uses secret sharing to transfer computations between participants without revealing intermediates and consolidates all validations into a dedicated phase to reduce communication complexity. Experiments with 7-member committees show its communication costs (for 10,000,000 entries) outperform existing protocols. A platform based on Spring Boot and MyBatis, integrating libscapi for core crypto operations, is developed with optimised architecture and visualisation tools. The protocol offers efficient, secure solutions for data sharing and collaboration, with significant theoretical and practical value.
  Keywords: multiparty secure computing; secret sharing; participant role switching.
  DOI: 10.1504/IJAHUC.2026.10076106
   
  

• Open Access

  Dynamic Load Balancing Model for Data Centres Based on Bidirectional Network Edge Detection Algorithm ( Free Full-text Access )

  by Diangang Hu, Xi Song, Bingling Gu, Kai Song, Xiaodong Wei 
  Abstract: To resolve link congestion and QoS deterioration caused by Elephant Flow conflicts in data centre networks under conventional multipath routing, this study proposes an innovative dynamic load balancing model. It combines Software-Defined Networking (SDN) for centralised control with a Transformer-BiLSTM-DNN deep learning framework: the Transformer encoder grasps global spatial correlations among traffic flows, BiLSTM retrieves intricate temporal variations, and DNN achieves deep feature integration for precise load state categorisation, enabling refined proactive traffic regulation. Evaluated on Google Borg Cluster Trace v2, the model excels in key metrics like Load Balancing Degree, Job Completion Time, and resource utilization, outperforming traditional baselines significantly. Ablation tests verify the essentiality of each component, providing a sturdy intelligent approach to enhance data center network efficiency and dependability.
  Keywords: Data center; Load balancing; Bidirectional network; Transformer; BiLSTM; SDN scheduling.
  DOI: 10.1504/IJAHUC.2026.10076107
   
  

• Open Access

  Modelling and Simulation for Biomechanical Responses of Acupuncture Points based on Multi-physics Field Coupling ( Free Full-text Access )

  by Xin Xiang, Dongming Li, Songlin Liu, Hongfeng Wang 
  Abstract: Accurate acupoint localisation is essential for standardised acupuncture therapy and intelligent systems, yet remains challenging due to soft-tissue variability, ambiguous anatomical boundaries, and cross-domain distribution shifts. This study proposes a unified perception framework that integrates multiple complementary mechanisms within a single localisation pipeline. The proposed ACUR-Net incorporates high-resolution feature representation, geometry-aware relational modelling, uncertainty-aware regression, and domain adaptation to address anatomical variability and domain heterogeneity in a coordinated manner. The underlying assumption is that reliable acupoint localisation benefits from the joint modelling of anatomical topology, cross-domain feature alignment, and prediction uncertainty, rather than from isolated architectural modifications. Experiments conducted on the AcuSim-FAcupoint dataset show that the proposed framework achieves improved feature transfer stability and spatial consistency. Comparative evaluations indicate that ACUR-Net outperforms PFLD, PIPNet, HRNet, and ViTPose in terms of NME, PCK@0.05, and OKS-mAP. The results suggest that multi-factor integration is effective for enhancing localisation robustness under realistic conditions.
  Keywords: Acupuncture point recognition; Multiphysics modeling; Cross-domain adaptation; Deep learning.
  DOI: 10.1504/IJAHUC.2026.10076574
   
  
• AI-Based Behaviour-Aware Path Risk Assessment for Wheelchair-Centric Smart Assistive Devices  
  by Chiao-Wen Kao, Shih-Tung Wang, Chi-Sheng Huang 
  Abstract: Mobility challenges in aging societies underscore the need for safer navigation solutions for wheelchair users, who often face hazards like uneven surfaces and indistinct curbs. Addressing these concerns, this study introduces an AI-based behavior-aware path risk assessment system designed for wheelchair-centric assistive devices. The system combines object detection and semantic segmentation to identify critical road features, such as sidewalks, curbs, and lanes. A Path Risk Assessment Module evaluates path safety using a scoring algorithm incorporating regional weighting and adjusting to varying environmental conditions. Integrating Internet of Behavior principles, the system adapts dynamically to user behaviors and conditions, offering personalized risk assessments. Tested on a custom dataset, the system demonstrated accurate real-time evaluations, with horizontal camera orientations excelling in dynamic scenarios and vertical setups suited for detailed analysis. These findings highlight its potential to enhance safety and mobility, with future work focusing on dynamic obstacles and multi-view integration for broader applications.
  Keywords: Behavior-Aware Systems; Smart Assistive Devices; Path Risk Assessment; Semantic Segmentation; Wheelchair-Centric.
  DOI: 10.1504/IJAHUC.2025.10070877
   
  
• Hierarchical Key Rotation, Isolation and Sensor-based Resilient Protocol for Wireless Sensor Networks  
  by Rounak Raman, Ayush Yadav, Vatsal Chhabra, Nisha Kandhoul, S.K. Dhurandher, Isaac Woungang, Han-Chieh Chao 
  Abstract: Ensuring a robust and secure wireless sensor network (WSN) remains a critical challenge, particularly against both internal and external threats. This paper presents a novel protocol hierarchical key rotation, isolation, and sensor-based resilient protocol (HKRISRP) designed to address the inherent security vulnerabilities in wireless sensor networks (WSNs). This approach tackles the critical challenges of dynamic threat detection and resource constraints by employing a hierarchical network architecture that integrates dynamic key rotation, lightweight encryption, and an adaptive cluster head re-election mechanism. This methodology not only enhances data confidentiality and integrity but also ensures energy efficiency and rapid threat isolation. Empirical results from both OMNeT++ simulations and hardware prototype evaluations demonstrate a throughput of 10,000 bps, an average remaining energy level of 82%, and a mean resolution time of 0.8 sec, significantly outperforming existing solutions. By introducing a robust framework for proactive threat prevention and isolation, HKRISRP offers a scalable and resilient solution for applications in military surveillance, border security, and industrial IoT environments.
  Keywords: Wireless sensor networks (WSNs); IoT security; Hierarchical network; Arduino; NRF module; Omnet++; Border security.
  DOI: 10.1504/IJAHUC.2025.10075499
   
  
• Energy Harvesting-Based Routing for Prolonging Sensor Node Lifespan in Underwater Networks Using Stochastic Network Calculus  
  by Vignesh S. R, Rajeev Sukumaran 
  Abstract: Underwater wireless sensor networks (UWSNs) are crucial for marine monitoring, disaster prediction, and underwater surveillance, but their performance is limited by energy constraints and harsh, unpredictable environments. Conventional deterministic models fail to capture underwater randomness, leading to inefficient energy management and unreliable communication. This work proposes an adaptive framework integrating stochastic network calculus (SNC) with piezoelectric energy harvesting (PEH) to enhance energy efficiency and network reliability. The model incorporates pH-based environmental variations within the SNC framework to dynamically regulate energy utilisation. Performance is evaluated against depth-based routing (DBR) and vector-based forwarding (VBF) using metrics such as: packet delivery ratio (PDR), packet loss ratio (PLR), end-to-end delay (E2E), throughput, path loss, and energy consumption. Results show significant improvements in network lifespan, energy efficiency, latency reduction, and data reliability. The proposed probabilistic approach provides a scalable and sustainable solution for real-time, next-generation UWSNs operating in dynamic aquatic environments.
  Keywords: Underwater Wireless Sensor Networks (UWSNs); Stochastic Network Calculus (SNC); Piezoelectric Energy Harvesting; pH-Based Modeling; Network Sustainability.
  DOI: 10.1504/IJAHUC.2025.10076088
   
  
• Wireless Channel Allocation in Smart Building using Cross-Interference Model  
  by Gabriel Galdino, Francisco Cardoso, Rafael Gomes 
  Abstract: IoT paradigm allowed a proliferation of connected wireless devices, that utilize various technologies such as Bluetooth, Zigbee, and Wi-Fi, all operating within the common 2.4GHz frequency band. However, sharing the same frequency poses challenges due to significant interference among these devices, making their coexistence in the same environment quite daunting mainly in smart buildings. Calculating interference between devices on different floors necessitates factoring in signal loss as it passes through ceilings or floors, along with distance-related signal attenuation between the devices. Within this context, this article proposes an interference model that is applied in an algorithm to allocate the channels of the devices in a smart building environment the model proposed aims to minimize the total interference of the environment between devices using different access technologies, improving the performance of the network and the applications that rely on it leading to a suitable expectation of the network capabilities.
  Keywords: Wireless Channel; Smart Building; Cross-Interference; Internet of Things.
  DOI: 10.1504/IJAHUC.2025.10076213
   
  
• Energy Consumption Algorithm for Unmanned Aerial Vehicle Communication Network in Marine Environment Monitoring based on Improved Deep Reinforcement Learning  
  by Jiayi Xin, Hongyan Xing 
  Abstract: Internet of Things (IoT) technology and Unmanned Aerial Vehicle(UAV) communication in marine environments can improve task execution efficiency and fault tolerance. Considering the complexity of the marine environment and the energy limitations of UAV network, this paper proposes an energy optimization algorithm based on an improved deep reinforcement learning (DRL) to minimize the total energy consumption of the network. The incremental gradient descent (ISGD) method is applied to DRL, where sample updates can effectively learn from streaming data in real-time interactions, enhance the interpretability and robustness of the strategy, and supplement the requirements of DRL for adaptive large-scale optimization of UAV communication networks. The simulation results show that compared with similar algorithms, the proposed ISGD-DRL algorithm can effectively reduce the energy consumption of unmanned aerial vehicle network nodes, has better convergence, and can better achieve the collection and transmission of marine environment monitoring information based on UAV communication network.
  Keywords: Unmanned Aerial Vehicle(UAV); Energy Consumption; Deep Reinforcement Learning(DRL); Incremental Subgradient Descent (ISGD).
  DOI: 10.1504/IJAHUC.2025.10077232
   
  
• Identity-Based Threshold Blind Signature Scheme in Standard Model  
  by Qu Yunyun, Xia Wang, Cuiju Ke, Miaomiao Yang, Lunzhi Deng 
  Abstract: The threshold blind signature scheme assigns signature capabilities to a group of signers and enables the user to acquire a messages signature issued by the signers without leaking the message content. Designing an efficient identity-based threshold blind signature scheme based on the standard model is of great significance. In this study, an identity-based threshold blind signature (IBTBS) scheme is proposed so as to achieve the decentralisation of the signer power and we prove our new schemes security which includes unforgeability, blindness and robustness in standard model. A performance comparative analysis is conducted between our new scheme and prior other schemes. The analysis shows that our new scheme is efficient which only needs three bilinear pairing operations in verification algorithm and our scheme possesses higher security. Our new scheme is identity-based which remove certificate management issue in the public key infrastructure (PKI).
  Keywords: threshold signature; blind signature; bilinear pairing; standard model.
  DOI: 10.1504/IJAHUC.2025.10077425
   
  
• Spectrum sensing using Reconfigurable Intelligent Surfaces with Acoustic Energy Harvesting  
  by Faisal Alanazi  
  Abstract: This paper investigates a novel spectrum sensing framework that integrates Reconfigurable Intelligent Surfaces (RIS) and acoustic energy harvesting in a cognitive radio environment. Specifically, a primary user (PU) harvests ambient acoustic energy to power its data transmission to a primary destination. The transmitted signal is reflected by a strategically placed RIS and subsequently received at the secondary user (SU), which performs spectrum sensing using an energy detection technique. The proposed system model captures the interplay between acoustic energy harvesting dynamics, RIS-assisted signal reflection, and the impact on the detection performance of the energy detector at the SU. Analytical expressions for detection probability is derived, taking into account acoustic energy harvesting. Simulation results validate the analytical findings and demonstrate the potential of the proposed architecture to enable efficient and green spectrum sensing in energy-constrained environments.
  Keywords: Spectrum sensing; RIS; acoustic energy harvesting.
  DOI: 10.1504/IJAHUC.2025.10077632
   
  
• DDPG-Based Joint Energy and Offloading Optimisation in UAV-Aided Mobile Edge Computing  
  by Changchun Qin, Yongzhi Ran, Fei Wang, Junwei Luo 
  Abstract: In this paper, we consider an unmanned aerial vehicles (UAV)-aided mobile edge computing (MEC) system, where a fixed-wing UAV provides computation resources for terminal devices (TDs). The UAV can effectively establish line-of-sight communication links with TDs. However, the limitations of energy capacity and transmission coverage of UAV and TDs are still challenges in UAV-aided MEC. The energy capacity affects the service lifetime of the UAV-aided MEC and the transmission coverage has an impact on the quality of service. To address these issues, we study a joint energy and offloading optimisation problem, where we aim to minimise the energy consumption of UAV and maximise the total offloaded data volume of TDs by optimising TDs transmission power and UAVs flight angle and speed. We propose a deep deterministic policy gradient (DDPG)-based algorithm to solve this problem. Simulation results show that our proposed algorithm has good convergence and is better than other algorithms.
  Keywords: Mobile edge computing (MEC); unmanned aerial vehicles (UAV); energy consumption; offloaded data volume; flight trajectory; deep deterministic policy gradient (DDPG).
  DOI: 10.1504/IJAHUC.2025.10077741
   
  
• Age-of-Computing Constrained Fairness-aware Energy Efficient Resource Allocation in MEC-assisted HSRNs  
  by Yeshen Li, Ke Xiong, Zhifei Zhang, Yingying Wu, Pingyi Fan 
  Abstract: This paper investigates the mobile edge computing (MEC)-assisted high-speed railway network (HSRN), where train users tasks can be offloaded to the MEC server deployed at the ground base station (GBS). The age of computing (AoC) is used to measure the timeliness of users task computation and the fairness-aware energy efficiency (FEE) is maximised by jointly optimising the task offloading ratio, channel selection factor, power allocation vector and the computing resource assignment vector at the MEC server subject to the constraints of AoC of the users tasks. To tackle such a non-convex mixed integer nonlinear programming (MINLP) problem, we propose a heterogeneous multi-agent twin delayed deep deterministic policy gradient-based resource allocation (HMATD3-RA) algorithm with a designed FEE-AoC reward function that linearly combines the violation of AoC and the FEE. Simulation results show that HMATD3-RA achieves the highest FEE-AoC reward compared with baselineswhile revealing the trade-off between FEE and AoC.
  Keywords: Mobile Edge Computing; Energy efficiency; Age of Computing; High-Speed Railway Networks; Multi-Agent Reinforcement Learning.
  DOI: 10.1504/IJAHUC.2025.10077876
   
  
• Optimizing Intrusion Detection Systems Using Ensemble Voting Classifiers: A Multi-Dataset Performance Analysis  
  by Sayantan Singha Roy, Amrita Bhadra, Munesh Chandra Trivedi, Awnish Kumar 
  Abstract: As cyberattacks continue to increase in size and complexity, so does the need to protect sensitive data. Ensemble voting classifier has risen as one of the most extensive methods to improve the performance of intrusion detection systems which play a pivotal role in securing networks. The basis of this study is to use six common classifiers [logistic regression (LR), random forest (RF), decision tree (DT), AdaBoost (ADB), Gaussian Naive Bayes (GNB), and K-nearest neighbours (KNN)] on three benchmark datasets (KDD-CUP, CIC-DDoS2019, UNSW-NB15) and compare the classifiers given their final metrics (accuracy, recall, precision and F1-score). This study has explored 62 ensemble configurations to assess their suitability as base classifiers. Results show that ADB and RF ensembles perform best on KDD-CUP, ADB-DT-GNB-RF combinations excel on CIC-DDoS2019, and ADB-RF achieve top results on UNSW-NB15. The study provides practical guidance for developing data-driven IDS solutions and enhances ensemble-based cybersecurity research.
  Keywords: Ensemble Machine Learning; Intrusion Detection Systems ; Ensemble Voting Classifiers; Benchmark Datasets; Comparative Analysis.
  DOI: 10.1504/IJAHUC.2026.10077881
   
  
• A Hybrid Ant Colony Genetic Optimisation Framework for Automated CNN Architecture and Hyperparameter Tuning in Image Classification  
  by Hong-Ren Chen, Mu-Yen Chen, Jia-Lang Xu, Yi-Syuan Wang, Po-Yen Hsu 
  Abstract: Hyperparameter and architectural optimisation remain critical challenges in deep learning, directly affecting model accuracy, generalisation, and efficiency. This study proposes a hybrid ant colony genetic optimisation (ACGO) algorithm for image classification, integrating the pheromone-guided search of ant colony optimisation with the genetic diversity mechanisms of crossover and mutation. A novel encoding scheme for convolutional neural network (CNN) hyperparameters enables automated tuning across datasets of varying complexity. The proposed ACGO is evaluated against five metaheuristic algorithms genetic algorithm, ant colony optimisation, coral reef optimisation, particle swarm optimisation, and Grey Wolf optimiser on CIFAR-10 and CIFAR-100. ACGO achieved 81.1% accuracy on CIFAR-10 and 52.7% on CIFAR-100 (an absolute improvement of approximately 4% and 3% against competing methods), demonstrating strong adaptability and computational efficiency. These results highlight ACGOs potential as a scalable, robust optimisation framework for deep learning-based image classification.
  Keywords: metaheuristic algorithms; hyperparameter optimization; image classification; model encoding; convolutional neural networks.
  DOI: 10.1504/IJAHUC.2026.10078301
   
  
• Resource Bargaining and Server Selection Framework in Multi-Access Edge Computing Platform  
  by Sungwook Kim 
  Abstract: In this study, we consider both the allocation of different types of resources in heterogeneous MEC servers and server selection. To solve these two control problems, we formulate a joint control framework based on the interactive mechanism. For the multi-resource allocation problem, we design the new compromise bargaining solution (CBS) to distribute different type resources. By combining well-known bargaining solutions, the CBS can provide a comprised consensus solution in a fair-efficient manner. To address the server selection problem, the novel multi-criteria selection (MCS) method is developed. Based on the combination of ideal solution and weighted average, the MCS method selects an appropriate MEC server for each device. In the heterogeneous MEC dynamics, the key insight of our proposed approach is to provide a collaborative control paradigm through the interaction of CBS and MCS processes
  Keywords: Multi-access edge computing; Multi-resource allocation problem; Compromise bargaining solutions; Multi-criteria selection; Cooperative game theory.
  DOI: 10.1504/IJAHUC.2025.10078326
   
  
• Path Planning of Multi Drone System for Disaster Analysis and Management  
  by Abhijeet Pandey, Bhavi Khator, J. Sathish Kumar 
  Abstract: Disasters often cause unpredictable damage that complicates emergency response efforts. While drones (UAVs) offer promise for accessing hard-to-reach areas, current systems lack robust autonomous navigation and 3D situational awareness needed for efficient disaster management. This paper presents a novel approach using multiple drones to collaboratively generate detailed 3D models and employs an advanced deep learning-based obstacle detection and 3D path-planning system. Extending traditional 2D algorithms into 3D, our method integrates new cost functions to optimise drone swarm navigation considering environmental and operational constraints. Using pre-disaster 3D maps and real-time data, the system enables faster, safer, and more efficient search and rescue operations. Validated in AirSim simulations, this framework significantly enhances UAV autonomy and coordination, addressing key limitations in current disaster response technologies and improving both pre- and post-disaster management strategies.
  Keywords: Path Planning; Disaster Management; Unmanned Aerial Vehicles.
  DOI: 10.1504/IJAHUC.2026.10078440