信息工程最新文献

{"title":"Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation","authors":"Haibo Xiao,&nbsp;Baoyun Gao,&nbsp;Shoukang Yu,&nbsp;Bin Liu,&nbsp;Sheng Cao,&nbsp;Shitong Peng","doi":"10.1007/s43684-024-00079-5","DOIUrl":"10.1007/s43684-024-00079-5","url":null,"abstract":"<div><p>Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO₂ equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00079-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons for biosecurity education from the International Nuclear Security Education Network","authors":"","doi":"10.1016/j.jobb.2024.09.002","DOIUrl":"10.1016/j.jobb.2024.09.002","url":null,"abstract":"<div><div>With the rapid advances in technology and life science, biological security is now at a defining moment. The mandate of the 2022 Biological and Toxin Weapons Convention 9th Review Conference emphasised the urgent need for new tools to strengthen the Convention. In this paper, we review the development and efforts of the International Nuclear Security Education Network (INSEN) to provide examples of best practice for implementation of the newly founded International Biological Security Education Network (IBSEN). Learning from the lessons of the INSEN, the sustainability of the network through continuous engagement of its members is essential for the further development of global biosecurity education.</div></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing","authors":"Zheyuan Chen,&nbsp;Jian Liu,&nbsp;Jian Li,&nbsp;Mukun Yuan,&nbsp;Guangping Yu","doi":"10.1007/s43684-024-00076-8","DOIUrl":"10.1007/s43684-024-00076-8","url":null,"abstract":"<div><p>Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00076-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved vision-only localization method for mobile robots in indoor environments","authors":"Gang Huang,&nbsp;Liangzhu Lu,&nbsp;Yifan Zhang,&nbsp;Gangfu Cao,&nbsp;Zhe Zhou","doi":"10.1007/s43684-024-00075-9","DOIUrl":"10.1007/s43684-024-00075-9","url":null,"abstract":"<div><p>To solve the problem of mobile robots needing to adjust their pose for accurate operation after reaching the target point in the indoor environment, a localization method based on scene modeling and recognition has been designed. Firstly, the offline scene model is created by both handcrafted feature and semantic feature. Then, the scene recognition and location calculation are performed online based on the offline scene model. To improve the accuracy of recognition and location calculation, this paper proposes a method that integrates both semantic features matching and handcrafted features matching. Based on the results of scene recognition, the accurate location is obtained through metric calculation with 3D information. The experimental results show that the accuracy of scene recognition is over 90%, and the average localization error is less than 1 meter. Experimental results demonstrate that the localization has a better performance after using the proposed improved method.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00075-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulations of a two-strain dengue model to investigate the efficacy of the deployment of Wolbachia-carrying mosquitoes and vaccination for reducing the incidence of dengue infections","authors":"","doi":"10.1016/j.jobb.2024.08.003","DOIUrl":"10.1016/j.jobb.2024.08.003","url":null,"abstract":"<div><p>This study investigated the usefulness of a two-serotype dengue mathematical model to gain insights into the effects of antibody-dependent enhancement and temperature on dengue transmission dynamics in the presence of vaccination and <em>Wolbachia</em>-carrying mosquitoes. In particular, the effects of temperature on the mosquito death and maturation rates in secondary infections were examined. A deterministic mathematical model was formulated and analysed to address this problem. The results suggest that controlling the population of aquatic mosquitoes is appropriate for reducing the incidence of secondary infections. Furthermore, the <em>wAu Wolbachia</em> strain was more effective in reducing secondary infections.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588933824000487/pdfft?md5=c755ea23a43690b6930ae1a984285d28&pid=1-s2.0-S2588933824000487-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stochastic epidemic model with time delays and unreported cases based on Markovian switching","authors":"","doi":"10.1016/j.jobb.2024.08.002","DOIUrl":"10.1016/j.jobb.2024.08.002","url":null,"abstract":"<div><p>Disease dynamics are influenced by changes in the environment. In this study, unreported cases (U), environmental perturbations, and exogenous events are included in the epidemic Susceptible–Exposed–Infectious–Unreported–Removed model with time delays. We examine the process of switching from one regime to another at random. Ergodicity and stationary distribution criteria are discussed. A Lyapunov function is used to determine several conditions for disease extinction. The spread of a disease is affected when transitioning from one random regime to another via sudden external events, such as hurricanes. The model and theoretical results are validated using numerical simulations.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588933824000475/pdfft?md5=f82949cbd4a1b36883019913a7b759e8&pid=1-s2.0-S2588933824000475-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT","authors":"","doi":"10.1016/j.bcra.2024.100195","DOIUrl":"10.1016/j.bcra.2024.100195","url":null,"abstract":"<div><p>Federated learning (FL) allows data owners to train neural networks together without sharing local data, allowing the industrial Internet of Things (IIoT) to share a variety of data. However, traditional FL frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposes a dual-blockchain based multi-layer grouping federated learning (BMFL) architecture. BMFL divides the participant groups based on the training tasks, then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000083/pdfft?md5=17c58876f9db0cb915d1b0b20cbe64c3&pid=1-s2.0-S2096720924000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable model for large-scale smart contract vulnerability detection","authors":"","doi":"10.1016/j.bcra.2024.100209","DOIUrl":"10.1016/j.bcra.2024.100209","url":null,"abstract":"<div><div>Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lack of interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features, thereby reducing the scales of features. The learning process could be accelerated significantly because of the reduction of the feature size. The experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each type is higher than 93% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerability is reduced by nearly 80% compared with that of its original.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141390225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis","authors":"","doi":"10.1016/j.bcra.2024.100207","DOIUrl":"10.1016/j.bcra.2024.100207","url":null,"abstract":"<div><div>As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How can the holder trust the verifier? A CP-ABPRE-based solution to control the access to claims in a Self-Sovereign-Identity scenario","authors":"","doi":"10.1016/j.bcra.2024.100196","DOIUrl":"10.1016/j.bcra.2024.100196","url":null,"abstract":"<div><p>The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity and enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. This paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000095/pdfft?md5=670501a0e4d21da648399fb2a95be292&pid=1-s2.0-S2096720924000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}