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Quantitative failure analysis for IoT systems: an integrated model-based framework

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第2期16卷 845-867页

作者： Abdulhamid, Alhassan Kabir, Sohag Ghafir, Ibrahim lei, ci Univ Bradford Fac Engn & Digital Technol Bradford BD7 1DP West Yorkshire England

The widespread adoption of Internet of Things (IoT) systems emphasises the need for dependable performance. However, challenges emerge due to the inadequacy of informal failure analysis models in evaluating the complex and distributed nature of IoT designs. These challenges can lead to inconsistencies, complicated system modifications, and extended development timelines. This paper presents a Model-Based Systems Engineering (MBSE) approach to create an integrated quantitative analysis framework for assessing the reliability of IoT systems. This approach simplifies the modelling and analysis of failure behaviours in IoT design, linking failure models to system design using standard engineering frameworks. Based on descriptions of IoT systems, the integrated approach establishes a traceability link between the system description and the analysable artefacts generated, such as stochastic Fault Tree Analysis (FTA) and Bayesian network (BN) models, which are essential for effective failure analysis of IoT systems. The approach demonstrates how to quantify system-level failures, identify and prioritise the weakest links in the design, and show how targeted modifications can improve the overall reliability of IoT systems. An illustrative case study on IoT is included to highlight the effectiveness and practical applicability of the proposed framework.

关键词： Internet of things Quantitative failure analysis Model-based systems engineering Fault tree analysis Bayesian networks

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MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction

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ACS OMEGA 2025年第12期10卷 12043-12053页

作者： ci, lei Li, Beilei Xu, Jiahao Peng, Sihua Jiang, Linhua Long, Wei Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Huzhou Weineng Environm Serv Co LTD Huzhou 313000 Peoples R China Univ Georgia Coll Publ Hlth Athens GA 30602 USA

In computer-aided drug design, molecular representation plays a crucial role. Most existing multimodal approaches primarily perform simple concatenation of various feature representations, without adequately emphasizing effective integration among these features. To address this issue, this study proposes a network framework that integrates multimodal representations using a multihead attention flow (MulAFNet). MulAFNet utilizes SMILES string representation and two levels of molecular graph representations: atom-level and functional group-level graph structure. Pretraining tasks are established for each of these three representations, which are then fused in downstream tasks to predict molecular properties. The experiments were conducted on six classification data sets and three regression data sets, demonstrating that the use of multiple molecular representations as input has a significant impact on the results. In particular, the excellent performance of our fusion method in molecular property prediction outperforms other state-of-the-art methods, proving its superiority. Additionally, comparative experiments on fusion methods and ablation studies, further validate the effectiveness of MulAFNet. The results demonstrate that multiple molecular feature representations provide a more comprehensive molecular understanding, and appropriate pretraining tasks enhance molecular property prediction.

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MMSG-DTA: A Multimodal, Multiscale Model Based on Sequence and Graph Modalities for Drug-Target Affinity Prediction

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2025年第2期65卷 981-996页

作者： Xu, Jiahao ci, lei Zhu, Bo Zhang, Guanhua Jiang, Linhua Ye-Lehmann, Shixin Long, Wei Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China Univ Paris Saclay Fac Med F-94276 Paris France

Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets-Davis, KIBA, and Metz-and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.

关键词： Prediction models

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Comparison of RT-qPCR With Branched DNA to Quantify a Lipid Nanoparticle-Encapsulated mRNA Therapeutic in Serum and Liver Tissue Samples From Nonclinical PK Studies

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AAPS JOURNAL 2025年第1期27卷 1-10页

作者： Ortiz, Jessica Brunner, Laura ci, lei Baek, Rena Jani, Darshana Marshall, Jean-Claude Pennucci, Jason Moderna Inc Cambridge MA 02142 USA

While the branched DNA (bDNA) assay is an established bioanalytical method for measurement of lipid nanoparticle (LNP)-encapsulated messenger RNA (mRNA) pharmacokinetic parameters, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has been considered as an alternative platform. RT-qPCR and bDNA platforms were compared for sensitivity, specificity, correlation, and overall assay performance using serum and tissue samples from 2 nonclinical mouse studies of a therapeutic mRNA candidate, LNP-PAH-mRNA, which encodes for human phenylalanine hydroxylase enzyme. Pharmacokinetic parameter noncompartmental analysis was completed using Phoenix WinNonlin. The assays were compared using simple linear regression and Bland-Altman analyses. Sensitivity ranged from 0.05 to 6.40 ng/mL for the bDNA assays, from 0.00000761 to 7.61 ng/mL in serum, and from 0.000179 to 179 ng/g in tissue for the RT-qPCR assay. Inter-assay accuracy was within +/- 10%, inter-assay precision was <= 10%, and the total error for both assays was <= 20%. RT-qPCR serum mRNA concentrations were 2- to fourfold lower compared with the bDNA assay, whereas tissue samples were comparable between assays. A linear relationship with - 0.37 to - 0.02 systematic bias demonstrated acceptable concordance. Bland-Altman plots demonstrated close equivalence, with a negative bias of < 0.5, and >= 95% of the data points were within the 95% limits of agreement. The comparison of the RT-qPCR with bDNA assay platforms for quantification of pharmacokinetic properties of an mRNA-LNP therapeutic has demonstrated acceptable concordance. This comparison reinforces the use of the RT-qPCR, a widely accessible strategy, as an alternative platform for the quantification of subsequent mRNA-LNP therapeutics.

关键词： bDNA bioanalytical mRNA therapeutics pharmacokinetics RT-qPCR

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Aristolochic acid IVa ameliorates arthritis in SKG Mice by regulating macrophage polarization and Th17/Treg balance

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PHYTOMEDIciNE 2025年 139卷 156557页

作者： Gao, Qin-wen Liu, Wei-ying Jawad, Mirza ci, lei Cao, Yi-yi Xi, Jing Wu, Jia-ying lei, Yu-yang Hu, Yu-shi You, Xin-yue Zhang, Xin-yu Fei, Jian Luan, Yang Shanghai Jiao Tong Univ Sch Med Sch Publ Hlth Shanghai 200025 Peoples R China Chengdu Univ Tradit Chinese Med Sch Publ Hlth Chengdu 610075 Peoples R China East China Normal Univ Inst Biomed Sci Ctr Bioinformat & Computat Biol Shanghai Key Lab Regulatory Biol Shanghai 200241 Peoples R China East China Normal Univ Sch Life Sci Shanghai 200241 Peoples R China SMOC Shanghai Engn Res Ctr Model Organisms Shanghai 201203 Peoples R China Tongji Univ Sch Life Sci & Technol Shanghai 200092 Peoples R China

Background: Aristolochic acids (AAs)-containing herbs have been used as medicinal remedies for thousands of years. However, exposure to AAI and AAII increases the risk of nephropathy and cancers. Our previous study identified AAIVa, an analogue without carcinogenicity or nephrotoxicity, exerted anti-inflammatory effects. Purpose: To explore AAIVa's anti-inflammatory mechanisms and assess its therapeutic potential in arthritis. Methods and results: In this study, we employed in vitro assays on RAW 264.7 cells and explored the underlying mechanisms of AAIVa's anti-inflammatory effect through transcriptome analysis, identifying macrophage polarization-associated genes, IL-17 signaling, and Rheumatoid Arthritis (RA) pathway. Also, we used BALB/ cAnSmoc-Zap70em(W163C)Smoc (SKG) mice, a model that spontaneously develops chronic arthritis closely resembling human RA, and revealed AAIVa's therapeutic potential in arthritis. AAIVa-treatment (10 mg/kg, i.g.) for 4 weeks protected SKG mice from mannan-accelerated arthritis symptoms, reducing inflammation and improving bone microstructure. We further isolated bone marrow-derived macrophages (BMDMs) and spleen primary cells from SKG and BALB/c mice to evaluate the impact of AAIVa on macrophage polarization and T cell differentiation. We found that AAIVa induced M2 macrophage polarization in BMDMs, and mitigated lipopolysaccharide-stimulated inflammation by increasing Tregs and decreasing Th17 cells. Subsequently, the elevation of M2 macrophages, increased Tregs expression and decreased Th17 cells in the ankle joints of SKG mice supported our in vitro observation. Conclusion: We provide first evidence that AAIVa exerts anti-arthritis effects, likely through modulation of macrophage polarization and restoration of the Th17/Treg balance. Our findings highlight AAIVa's mechanism of action and AAIVa's potential as a therapeutic candidate for autoimmune arthritis.

关键词： Aristolochic acid IVa Anti-inflammatory effect Rheumatoid arthritis Macrophage polarization Th17/Treg balance

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Quantitative Cybersecurity Analysis Framework for Cyber Physical Systems: A Conceptual Approach

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the Computer Society 2025年

作者： Abdulhamid, Alhassan Kabir, Sohag Ghafir, Ibrahim lei, ci Hindi, Khalil El Hammoudeh, Mohammad Faculty of Engineering and Digital Technologies University of Bradford BradfordBD7 1DP United Kingdom College of Computer and Information Sciences King Saudi University P.O. Box 70474 Riyadh11543 Saudi Arabia School of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom

Cyber-physical systems (CPS) are indispensable in various sectors, enabling convenient and efficient processes in today’s rapidly evolving technological landscape. However, the integration of internet-enabled components with physical processes exposes CPS to numerous security threats, rendering them susceptible to potential cyber-attacks. This paper presents a quantitative analysis framework for evaluating the security attributes of CPS conceptual design. Focusing on CPS design architecture, the framework models and quantifies security attributes by considering various dimensions. The paper demonstrates the integration of qualitative expert input into a fuzzy logic system to address the challenges and uncertainties associated with vulnerability data in CPS security quantification. Additionally, it examines the statistical dependence of basic attack steps (BASs) and their impact on the overall system security analysis, taking into account the intricate connectivity of CPS and the vulnerabilities that attackers could exploit. The novelty of the proposed framework lies in its integrated approach to modelling and quantifying security attributes in the CPS environment while considering uncertainties in vulnerability data and dependencies between security events. The computation of statistical and stochastic dependencies among BASs is achieved by mapping the attack tree (AT) to a higher statistical model of the Bayesian network (BN) model. The application of this framework was demonstrated using an intelligent glucose monitoring and insulin administration system (IGMIAS). The framework’s general nature makes it adaptable for quantifying cybersecurity behaviours in any CPS environment. ©2020 IEEE.

关键词： Fuzzy logic

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结合分子图与分子指纹对比的分子性质预测

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网络新媒体技术 2025年第2期14卷 40-49页

作者：慈磊蒋林华湖州师范学院信息工程学院湖州313000

分子性质预测已经成为新药研发过程中十分重要的一个过程,然而还存在单一分子表示信息不完全、标签分子数量稀缺的问题。为了解决这些问题,提出一种结合分子图与分子指纹的对比学习框架。根据拓扑信息将无标签分子进行聚簇,得到这些簇... 详细信息

分子性质预测已经成为新药研发过程中十分重要的一个过程,然而还存在单一分子表示信息不完全、标签分子数量稀缺的问题。为了解决这些问题,提出一种结合分子图与分子指纹的对比学习框架。根据拓扑信息将无标签分子进行聚簇,得到这些簇中的中心分子,接着利用中心分子的2种不同表示形式与数据集中的数据进行对比学习。在对比学习过程中,根据2种不同的分子表示信息,在化学空间中拉近相似结构的分子,疏远结构上不同的分子,同时能够丰富分子的表示信息。实验结果显示,所提出的方法能够有效提高分子表示的特征信息,同时在6个公开数据集上平均性能比目前先进的模型高1.1%。

关键词：分子性质预测预训练对比学习分子指纹多种表示学习

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Pb, Cu botanogeochemical anomalies and toxic effects on plant cells in Pb-Zn (Sn) ore fields, Northeast Guangxi Autonomous Region, China

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Chinese Journal Of Geochemistry 2007年第3期26卷 329-332页

作者： SONG ci＇an lei Liangqi YANG Qijun Department of Resources and Environment Engineering Guilin University of Technology Guilin 541004 China

In the Lingchuan-Daoping and Xinglu Pb-Zn ore fields in northern and eastern Guangxi Autonomous Region, Pb, Cu botanogeochemical anomalies may be ascribed to the excessive amounts of Pb and Cu taken up by the root system of plants, such as China fir (Cunninghamia lanceolata Lamb. Hook), mason pine (Pinus massoniana Lamb.) and bracken fern (Pteridium aquilinum var. latiusculum). Under transmission electron microscope (TEM), the excess Pb, Cu in the leaf cells of the plants are present as high electron-density substances, which were precipitated in the leaf cells, causing phytotoxic effects by deforming and injuring cellular tissues. The sorts of toxic elements accumulating in the leaf cells are consistent with those of the botanogeochemically anomalous elements in the polluted soil where the plants grow. In addition, the plants may also be capable of resisting the invasion of excess Cu (and Pb) .

关键词：铅铜植物化学植物性中毒

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Physical Role Limitation - It's Classification and Prediction using Machine Learning 19

Physical Role Limitation - It's Classification and Predictio...

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International Conference on Big Data and Education (ICBDE)

作者： Azad, Sadaf Al Fanah, Muna lei, ci Univ Bradford Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9781450361866

The focus of this study is to define the classification of physical role limitation in patients and predict patient physical role limitation based on other health factors, using machine learning. For this purpose, patients' records in National Child Development Study Dataset (NCDS), which started in 1958, were used. This study performs classification of patients' existing records and categorization of new records on the basis of trained model by implementing data mining techniques, including linear discriminant analysis (LDA), support vector machine (SVM), Decision Tree (CART), Random Forest, and k-nearest neighbor (KNN). Findings of this research will help medical practitioners to classify patients on the basis of other valuable health related information and observe the physical role limitations in patients at age of 50 and above. For instance, knowing the general health scales of patients can help practitioners to predict the physical activity scale in patients, and thus help practitioners in e.g. risk assessment and drawing up treatment and care plans. The contribution of this study is to utilize a study covering 55 years' observations of the candidates in order to help practitioners to identify and predict the physical role limitation in people when they turn 50 and over on the basis of other health related information. Of the five classifiers applied in this study, it can be confidently said that Random Forest is the best classifier with 72.48% accuracy to be used in data situations like NCDS.

关键词： Classification and regression (CART) data mining process classification models machine learning physical role limitation

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Computational Complexity Analysis of Decision Tree Algorithms 38th

Computational Complexity Analysis of Decision Tree Algorithm...

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38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI)

作者： Sani, Habiba Muhammad lei, ci Neagu, Daniel Univ Bradford Bradford W Yorkshire England

ISBN: (纸本)9783030041915;9783030041908

Decision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets.

关键词： Classification Decision trees Complexity

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