Arabian Journal for Science and Engineering最新文献

{"title":"Generative Adversarial Networks for Intrusion Detection Systems: A Comprehensive Survey of Applications, Challenges, and Research Directions","authors":"Mohammad Alauthman,&nbsp;Nauman Aslam,&nbsp;Ahmad Al-Qerem,&nbsp;Amjad Aldweesh,&nbsp;Pradorn Sureephong","doi":"10.1007/s13369-026-11103-6","DOIUrl":"10.1007/s13369-026-11103-6","url":null,"abstract":"<div><p>The evolving threat landscape demands intrusion detection systems that adapt quickly to novel attack patterns and operate across heterogeneous environments. Recent studies show that Generative Adversarial Networks (GANs) can improve intrusion detection performance by generating synthetic attack traffic, balancing imbalanced datasets, enhancing adversarial robustness, and serving as anomaly detectors. This survey provides a comprehensive and systematic review of GAN-based intrusion detection system (IDS) research, analyzing the architectures employed—including Wasserstein GANs, conditional GANs, self-attention GANs, and specialized multi-generator designs—together with their applications, datasets, and evaluation metrics. Unlike previous surveys, we extend the scope to resource-constrained Internet of Things (IoT) and federated scenarios, where lightweight and tabular GANs can process sensor data and operate on edge devices. We also examine deployments in software-defined networking environments. We propose a unified evaluation framework that reports class-wise precision, recall and macro-F1-scores, per-attack metrics, computational cost, and statistical similarity tests, and we emphasize the need for interpretable and multi-modal approaches that fuse network flows with logs or threat intelligence. Emerging paradigms including GANs combined with large language models, quantum GANs, diffusion models, and reinforcement learning are surveyed, and open challenges such as training instability, mode collapse, hyper-parameter tuning, and ethical dual-use concerns are discussed. By synthesizing recent advances and outlining future research directions, this survey provides a comprehensive and forward-looking reference for practitioners and researchers developing robust, privacy-preserving, and adaptive GAN-based intrusion detection systems.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"179 - 203"},"PeriodicalIF":2.9,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breathing Cycle Detection for Respiratory Tele-health Systems","authors":"Tasnim Nishat Islam,&nbsp;Mohamed Younis,&nbsp;Wassila Lalouani,&nbsp;Lloyd Emokpae,&nbsp;Roland Emokpae Jr.","doi":"10.1007/s13369-025-11052-6","DOIUrl":"10.1007/s13369-025-11052-6","url":null,"abstract":"<div><p>Recent advancements in wearable devices have enabled the acquisition of lung sounds in real time. By analyzing these signals, key indicators such as respiratory cycles and heart sound components can be extracted, hence enabling the development of tele-health solutions for remote assessment of pulmonary conditions. Particularly, detecting respiratory cycles within the collected sound data plays a crucial role in both clinical and diagnostic applications. Accurate identification of breathing patterns facilitates the assessment of respiratory function and supports early detection of anomalies, including COPD, pneumonia, asthma, and COVID-19. In this paper, we promote a two-step process that first estimates breathing sound signal envelope (in time domain) and then analyzes the envelope peaks/valleys to calculate the respiratory cycle. Three methods that follow such a process are proposed. We examine the practicality, scalability, and efficacy of these methods in both healthy and pathological cases, highlighting their potential for integration into real-world respiratory monitoring and screening systems. We further evaluate their performance using the public dataset ICBHI-2017, which allows comparative analysis of various respiratory conditions.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"321 - 339"},"PeriodicalIF":2.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-025-11052-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Voltammetric Detection of Selected Antibiotic Residues in Dairy Products Utilizing Iron-doped Zeolite-Modified Carbon Paste Electrode","authors":"Asma Alnuaimi,&nbsp;Abdel-Nasser Kawde,&nbsp;Abdelaziz Elgamouz,&nbsp;Saad Ben Haiba,&nbsp;Abderrazek El-Kordy","doi":"10.1007/s13369-025-10928-x","DOIUrl":"10.1007/s13369-025-10928-x","url":null,"abstract":"<p>Antibiotic residues in food threaten public health by promoting antimicrobial resistance. Chloramphenicol (CAP), banned in food-producing animals, can be rapidly and sensitively detected using cost-effective electrochemical techniques. In this study, an iron-doped zeolite (Fe/ZSM-5) modified carbon paste electrode (CPE-30 wt%/Fe-ZSM-5) was fabricated and characterized electrochemically using cyclic voltammetry. The sensor exhibited the highest current response of 202.03 µA and the largest electroactive surface area of 0.162 cm² in the Fe(CN)₆³⁻/Fe(CN)₆⁴⁻ redox system, with a low charge transfer resistance (R_ct = 285 Ω), confirming superior conductivity. Linear sweep voltammetry (LSV) was employed for CAP detection using CPE-30 wt%/Fe-ZSM-5, which produced a maximum response of 73.65 µA, significantly higher than the unmodified CPE (6.51 µA), GPE (6.39 µA), and GCE (4.43 µA). Comparison of different electrochemical techniques using CPE-30 wt%/Fe-ZSM-5 revealed LSV as the most sensitive (71 µA), outperforming CV (38.91 µA), DPV (10.84 µA), NPV (23.46 µA), and SWV (13.64 µA). Optimization of scan rate and pH yielded maximum responses at 100 mV/s (5.32 µA) and pH 7.0, respectively. The sensor demonstrated high selectivity in the presence of potential interferents at twice the molar concentration of the CAP, with excellent repeatability (3.24% RSD), reproducibility (3.05% RSD), and stability (98.79%). Analytical performance showed a linear dynamic range of 1–1000 µM, a LOD = 1.4 µM, a LOQ = 4.3 µM, and a sensitivity of 1.833 µA/µM·cm². Recovery studies in spiked full-fat milk (88.37–107.71%) demonstrated accurate quantification of CAP, confirming the sensor’s practical applicability for food safety monitoring and antibiotic residue detection.</p>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"435 - 449"},"PeriodicalIF":2.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphene-Based Nanomaterials for Strain Detection in Smart Concrete","authors":"Irfan Hanief,&nbsp;Muhamad Alhajja,&nbsp;Salman Sajan,&nbsp;Paul Isu,&nbsp;Mahtab Alam,&nbsp;Shamsad Ahmad,&nbsp;Asad Hanif","doi":"10.1007/s13369-025-10981-6","DOIUrl":"10.1007/s13369-025-10981-6","url":null,"abstract":"<div><p>The necessity to sustain structural integrity throughout the service life of a structure has driven extensive research in structural health monitoring (SHM) using various materials to enhance safety and durability. Graphene and its derivatives, including graphene oxide (GO) and reduced graphene oxide (rGO), have seen substantial SHM applications. In this paper, findings from various studies are critically reviewed to evaluate the efficacy of GO and rGO in SHM, specifically for strain detection in self-sensing concrete. It is found that GO and rGO, when dispersed well in the cementitious matrix, enhance concrete mechanical properties as a piezoelectric material, integrate with the cement microstructure, and enable non-destructive self-sensing, outperforming most existing self-sensing materials. Additionally, GO and rGO are effective energy dissipators, enhancing the fracture energy in concrete composites by reducing crack propagation. When appropriately proportioned, the mechanical, thermal, and electrical properties of these materials make them suitable for self-sensing material applications in smart concrete.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"503 - 540"},"PeriodicalIF":2.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrodeposited Medium- and High-Entropy Alloys: A Review of Recent Advances in Electrochemical Energy-Related Applications","authors":"Samavia Rafiq,&nbsp;Mohammed A. Gondal,&nbsp;Farhan Arshad,&nbsp;Munerah Abdullah Almessiere","doi":"10.1007/s13369-025-10912-5","DOIUrl":"10.1007/s13369-025-10912-5","url":null,"abstract":"<div><p>Medium-entropy alloys (MEAs) and high-entropy alloys (HEAs) have emerged as promising materials for advanced electrocatalytic applications, offering a unique balance between compositional complexity and structural tunability. This review highlights the fundamental design principles of MEAs and HEAs, focusing on their configurational entropy, mechanical strength, corrosion resistance, and magnetic properties. It also covers different synthesis approaches used to produce entropy alloys, including additive manufacturing, powder metallurgy, magnetron sputtering, high-pressure torsion, and electrodeposition. In comparison, this review specifically emphasizes the advantages and detailed types of electrodeposition methods for synthesizing MEA and HEA. The electrodeposition method is highlighted as a flexible, scalable approach for synthesis, enabling precise control of film thickness, surface features, and elemental distribution in ambient conditions. Various electrodeposition methods, including chronopotentiometry (constant current), chronoamperometry (constant potential), and pulse electrodeposition, are critically examined for their role in fabricating nanostructured MEA and HEA electrocatalysts. The multifunctional capabilities of these alloys are explored across key electrochemical applications, including water splitting, alcohol oxidation, oxygen reduction, and carbon dioxide reduction. By integrating the intrinsic compositional complexity of MEAs and HEAs with the synthesis flexibility of electrodeposition, these materials offer a powerful platform for designing tailored catalytic interfaces and enhancing performance in diverse electrochemical energy conversion systems.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"353 - 393"},"PeriodicalIF":2.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Approaches to Evaluating ADHD Using EEG Data: RNN, GRU, and LSTM Models","authors":"Nouf Jubran AlQahtani,&nbsp;Manar Gherbi,&nbsp;Ali Al-Shaikhi,&nbsp;Mohamed A. Mohandes","doi":"10.1007/s13369-025-10984-3","DOIUrl":"10.1007/s13369-025-10984-3","url":null,"abstract":"<div><p>Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that can negatively affect daily functioning. In this study, we investigated EEG–based ADHD classification using recurrent neural network (RNN), gated recurrent unit (GRU), and long short–term memory (LSTM) models to explore potential neural features associated with underlying cognitive mechanisms. We analyzed EEG data from 61 children with ADHD and 60 healthy controls (aged 7–12 years) during a visual attention task. EEG signals were recorded from 19 channels at a sampling rate of 128 Hz. Prior to applying machine learning algorithms, the data were pre–processed using a 50 Hz notch filter, a band–pass filter with cut–off frequencies of 4–40 Hz, and independent component analysis (ICA). Feature extraction focused on spectral power across four frequency bands (theta, alpha, beta, and gamma), as well as the frontal–parietal theta–alpha ratio, mean, standard deviation (SD), entropy, and root mean square (RMS). RNN, GRU, and LSTM models were then evaluated and compared. The results showed that the LSTM model outperformed the other architectures, achieving an accuracy of 64.53% in distinguishing individuals with ADHD from controls. Consistent with well–documented ADHD–related deficits in executive functions and attention modulation, features from the frontal and parietal regions were the most discriminative. Overall, this study demonstrates the potential of machine–learning–based approaches for EEG–driven ADHD detection.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"287 - 319"},"PeriodicalIF":2.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methane as a Greenhouse Gas: Capture, Underground Storage, Utilization, and Impacts","authors":"Arshad Raza,&nbsp;Guenther Glatz,&nbsp;Mohamed Mahmoud,&nbsp;Saad Alafnan","doi":"10.1007/s13369-025-10987-0","DOIUrl":"10.1007/s13369-025-10987-0","url":null,"abstract":"<div><p>Given methane’s high energy content and global warming potential, underground methane storage (UMS) offers a promising strategy for both energy security and greenhouse gas reduction. This review examines methane capture, storage, and utilization, with a focus on its physicochemical behavior in geological media. Key topics include capture technologies, methane’s physical and chemical properties, storage formations, and end uses like grid balancing and energy recovery. The review also identifies research gaps and discusses the future role of UMS in climate and energy solutions. Despite existing challenges, UMS is highlighted as essential for enhancing energy security, lowering emissions, and supporting the global energy transition.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"1087 - 1101"},"PeriodicalIF":2.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Variations in P-Wave Velocity as a Function of Pressure in Carbonates: An Artificial Neural Network Approach Incorporating the Impact of Rock Properties","authors":"Ammar El-Husseiny","doi":"10.1007/s13369-025-11024-w","DOIUrl":"10.1007/s13369-025-11024-w","url":null,"abstract":"<div><p>This study investigates the applicability of using artificial neural networks (ANN) to predict the variations in P-wave velocity (<i>V</i>_p) as function of pressure (<i>P</i>) changes in carbonate rocks. Predicting this <i>V</i>_p–<i>P</i> relationship is critical for time-lapse seismic interpretation and geomechanics-related applications. Traditional laboratory measurements to determine <i>V</i>_p–<i>P</i> relationship are time-consuming, while existing empirical regression models often overlook the influence of petrophysical properties on <i>V</i>_p–<i>P</i> trends and lack adequate prediction accuracy. A comprehensive dataset of 363 carbonate core samples (1624 data points in total for measured <i>V</i>_p at varying <i>P</i>), covering diverse geological settings (different regions) and microstructures, was compiled from both new laboratory experiments and published studies. The ANN model incorporated petrophysical parameters including initial velocity, porosity, bulk density, mineralogy, and permeability. Results for the entire combined dataset demonstrate that ANN outperforms regression, reducing the root-mean-square error (RMSE) by up to 35% (from regression RMSE of 158 m/s) when using initial velocity alone as an input. Incorporating petrophysical properties into ANN improved prediction accuracy, with further error reduction reaching an RMSE of 48 m/s. ANN models trained on individual datasets achieved the lowest errors, highlighting their robustness for region-specific applications, while leave-one-out tests confirmed predictive reliability for unseen datasets. Despite the complexity of the <i>V</i>_p–<i>P</i> relationships in carbonates, this study shows the effectiveness of using ANN model to address such a problem when incorporating petrophysical rock properties as inputs. The study offers a workflow for integrating ANN-based method with petrophysics, potentially reducing experimental requirements while improving subsurface characterization.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"255 - 271"},"PeriodicalIF":2.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oxidative Band Alignment Shift in AgO- and ZrO2-Modified Fibrous Silica Iron for Dual-Pollutant Photocatalysis","authors":"Nur Izzati Hannani Hazril,&nbsp;Aishah Abdul Jalil,&nbsp;Nurul Sahida Hassan,&nbsp;Liew Shi Yan,&nbsp;Muhammad Hakimi Sawal,&nbsp;Nik Muhammad Izzudin,&nbsp;Muhammad Hafizuddin Mohd Sofi,&nbsp;Nurfatehah Wahyuny Che Jusoh,&nbsp;Faiz Arith,&nbsp;Sudiyarmanto Sudiyarmanto","doi":"10.1007/s13369-025-11011-1","DOIUrl":"10.1007/s13369-025-11011-1","url":null,"abstract":"<div><p>Industrial wastewater treatment remains a pressing challenge owing to the coexistence of hazardous heavy metals and persistent organic dye contaminants that resist conventional remediation approaches. In this work, a fibrous silica iron (FSFe) catalyst was successfully incorporated with metal oxides (AgO, ZrO₂) via a microemulsion-assisted electrolysis method, leading to a uniform dispersion of the metal oxides across the fibrous framework. Among the synthesized composites, AgO/FSFe exhibited superior photocatalytic activity in dual removal of hexavalent chromium (Cr(VI)) and methyl orange (MO) compared to that of ZrO₂/FSFe and pristine FSFe. Structural characterization confirmed the successful formation of Si–O–M bonds in AgO/FSFe and the electron transfer from AgO to FSFe, indicating strong interfacial interactions. This improvement results from an oxidative shift in the conduction and valence bands, which enables optimal band alignment and a stable <i>p</i>–<i>n</i> heterojunction structure, thereby minimizing electron–hole recombination, as supported by photoluminescence (PL), electrochemical impedance spectroscopy (EIS), and Mott–Schottky analyses. Scavenger experiments revealed that photogenerated electrons were the main contributors to Cr(VI) reduction, whereas ·OH radicals dominated MO degradation. Under optimized conditions (pH 3, catalyst dosage 0.25 g L⁻¹, initial concentrations of 20 mg L⁻¹ Cr(VI) and 10 mg L⁻¹ MO), the 10AgO/FSFe catalyst achieved simultaneous removal efficiencies of 64.37% for Cr(VI) and 99.45% for MO while maintaining its performance over five successive cycles, outperforming the photolysis and single-pollutant systems. Furthermore, the catalyst demonstrated versatility by effectively degrading additional organic dyes. This structure enhances charge separation and enables the simultaneous removal of heavy metals and organic pollutants, establishing AgO/FSFe as a durable and sustainable wastewater photocatalyst.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"451 - 468"},"PeriodicalIF":2.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-025-11011-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Understanding the Physical Volcanology of One of the Largest Monogenetic Volcanic Provinces on the Earth, Western Arabian Peninsula","authors":"Károly Németh","doi":"10.1007/s13369-025-10978-1","DOIUrl":"10.1007/s13369-025-10978-1","url":null,"abstract":"<div><p>Western Arabia, within the territory of the Kingdom of Saudi Arabia, contains at least 13 large monogenetic volcanic fields, collectively forming one of the largest Cenozoic monogenetic volcanic provinces on earth. The region includes at least 3000 individual, small-volume, predominantly mafic volcanoes. A review indicates that volcanology research has increased in the number of peer-reviewed, international publications since the early 2010s, coinciding with the Volcanic Risk of Saudi Arabia (VORiSA) programme (~ 2012–2015)—a collaboration involving King Abdulaziz University in Jeddah, as well as Auckland University and Massey University in New Zealand. This research mainly focused on the Rahat Volcanic Field, one of the largest and longest-lived in the region, situated between the major Saudi cities of Jeddah, Makkah, and Al Madinah. The last recorded eruption, which took place in 1256 CE near Al Madinah, led to geological mapping under the joint programmes of the United States and Saudi Geological Surveys (USGS–SGS) (~ 2015–2018). These efforts, primarily for volcanic hazard assessment, have resulted in the most comprehensive volcanological summary of any Saudi Arabian volcanic field, partly due to the outcomes of the VORiSA programme. Despite these advances, due to the extensive territorial coverage, diversity of volcanism, and the large number of volcanoes, alongside a limited focus on volcano-scale geological research, several critical knowledge gaps remain. These include a lack of spatio-temporal understanding of volcanic events within individual fields, detailed studies on specific volcanoes at the event scale using advanced analytical techniques, investigations into the impacts of dispersed volcanic ash, and insufficient evidence-based scenario modelling for potential future eruptions. It is also noted that future work should focus on integrating high-resolution geological mapping, developing eruption scenarios, and conducting hazard simulations.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"51 -","pages":"223 - 253"},"PeriodicalIF":2.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}