Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Zezhi Wen¹, Kai Zhang¹, Shanlin Liang², Liqiong Chen^1,*, Zijian Xiong¹

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.066408 - 08 January 2026

Abstract Maintaining the structural integrity of parallel natural gas pipelines during leakage-induced jet fires remains a critical engineering challenge. Existing methods often fail to account for the complex interactions among heat transfer, material behavior, and pipeline geometry, which can lead to overly simplified and potentially unsafe assessments. To address these limitations, this study develops a multiphysics approach that integrates small-orifice leakage theory with detailed thermo-fluid-structural simulations. The proposed framework contributes to a more accurate failure analysis through three main components: (1) coupled modeling that tracks transient heat flow and stress development as fire conditions evolve; (2)… More >

Xiaolong Li, Hui Chen, Yingwen Liu, Peng Yang^*

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1767-1788, 2025, DOI:10.32604/fhmt.2025.070537 - 31 December 2025

Abstract Hazardous gas intrusion in tightly sealed and geometrically complex confined spaces, such as armored tanks, poses a critical threat to occupant health. The intricate internal structure of these systems may lead to non-intuitive pollutant transport pathways. However, the spatial and temporal evolution of these structures, as well as the intrinsic mechanisms of the purification systems, remain poorly elucidated. In this study, a high-fidelity, transient three-dimensional computational fluid dynamics (CFD) model was developed to simulate the leakage and dispersion of carbon monoxide (CO) and nitrogen dioxide (NO₂) using the RNG k-ε turbulence model. Scenarios with and without… More > Graphic Abstract

Mingyu Cai^1,2, Xingchun Li^1,2, Kunfeng Zhang^1,2,*, Shugang Yang^1,2, Shuangxing Liu^1,2, Ming Xue^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-2, 2025, DOI:10.32604/icces.2025.010490

Abstract Deep saline aquifers account for more than 90% of the global theoretical geological CO₂ sequestration capacity, making them the dominant choice for large-scale CO₂ storage. These aquifers offer vast storage potential, especially in comparison to oil and gas reservoirs, which are often considered for CO₂ geological sequestration. Despite their significant storage capacity, deep saline aquifers face several challenges that hinder their practical application. In particular, the lack of adequate geological infrastructure and exploration conditions for deep saline aquifers presents major obstacles to the effective monitoring of CO₂ migration and predicting leakage risks. These challenges are compounded by… More >

Eleena Sarah Mathew^*

Journal on Artificial Intelligence, Vol.7, pp. 347-363, 2025, DOI:10.32604/jai.2025.069841 - 06 October 2025

Abstract This review paper explores advanced methods to prompt Large Language Models (LLMs) into generating objectionable or unintended behaviors through adversarial prompt injection attacks. We examine a series of novel projects like HOUYI, Robustly Aligned LLM (RA-LLM), StruQ, and Virtual Prompt Injection that compel LLMs to produce affirmative responses to harmful queries. Several new benchmarks, such as PromptBench, AdvBench, AttackEval, INJECAGENT, and RobustnessSuite, have been created to evaluate the performance and resilience of LLMs against these adversarial attacks. Results show significant success rates in misleading models like Vicuna-7B, LLaMA-2-7B-Chat, GPT-3.5, and GPT-4. The review highlights limitations… More >

Shiyao Peng¹, Hanwen Zhang¹, Chong Chai¹, Shilong Xue², Xiaobin Zhang^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1099-1112, 2025, DOI:10.32604/fdmp.2025.060452 - 30 May 2025

Abstract The diffusion of hydrogen-blended natural gas (HBNG) from buried pipelines in the event of a leak is typically influenced by soil properties, including porosity, particle size, temperature distribution, relative humidity, and the depth of the pipeline. This study models the soil as an isotropic porous medium and employs a CFD-based numerical framework to simulate gas propagation, accounting for the coupled effects of soil temperature and humidity. The model is rigorously validated against experimental data on natural gas diffusion in soil. It is then used to explore the impact of relevant parameters on the diffusion behavior… More >

Phi Thuong Le¹, Huy Quoc Le², Viet Cuong Trinh^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4741-4756, 2025, DOI:10.32604/cmc.2025.064486 - 19 May 2025

Abstract Attribute-based encryption () is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes. is widely applied in cloud storage, file sharing, e-Health, and digital rights management. schemes rely on hard cryptographic assumptions such as pairings and others (pairing-free) to ensure their security against external and internal attacks. Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent. One common internal attack is the attribute collusion attack, in which users with different attribute keys collaborate to decrypt data they could not… More >

Jiewen Liu¹, Bing Chen^1,2,*, Baolu Xue¹, Mengya Guo¹, Yuntao Xu¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1857-1877, 2025, DOI:10.32604/cmc.2024.057814 - 17 February 2025

Abstract Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack… More >

Yanbo Shao¹, Xuewen Cao^1,*, Wei You¹, Shan Zhao¹, Zilong Nan², Jiang Bian^1,3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2867-2885, 2024, DOI:10.32604/fdmp.2024.053774 - 23 December 2024

Abstract Transporting massive quantities of carbon dioxide through a pipeline in its supercritical state is extremely convenient. Because of the unique properties of supercritical carbon dioxide, however, leakage occurring in such conditions can be extremely intricate, resulting in the dispersion area following leakage being influenced by numerous factors. In this study, this problem is addressed in the frame of the so-called Unified Dispersion Model (UDM), and various influential parameters are considered, namely, leakage pressure, leakage temperature, leakage aperture, leakage angle, atmospheric stability, wind speed, and surface roughness. The results show that the supercritical carbon dioxide dispersion More >

Pengfei Duan^1,*, Luling Li¹, Jianhui Liu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012431

Abstract The supply of hydrogen-blended natural gas to civil and industrial users can assist downstream firm to achieve carbon emission reduction, and ensure energy security as an alternative gas source. This application mode has been widely concerned by urban gas enterprises. This paper focuses on the leakage problem of hydrogen-blended pipelines in utility tunnel due to corrosion and other reasons. Using dimensional analysis method, a model experiment is designed to verify that the three-dimensional compressible fluid model coupled with transport equations can effectively simulate the concentration change of hydrogen-blended natural gas after leakage in the utility… More >