信息工程最新文献

{"title":"The association between mycotic pneumonia and neonatal septicemia","authors":"Salman Khalid Salman,&nbsp;Yasir Mufeed Abdulateef,&nbsp;Sawsan Qahtan Taha Al-Quhli","doi":"10.1016/j.jobb.2024.06.002","DOIUrl":"https://doi.org/10.1016/j.jobb.2024.06.002","url":null,"abstract":"<div><h3>Background</h3><p>Candida species are the fourth most common etiological agents of late-onset infection in the neonatal intensive care unit (NICU) and are responsible for considerable morbidity and mortality.</p></div><div><h3>Objectives</h3><p>From November 2023 to February 2024, we investigated the association of mycotic pneumonia with septicemia in 60 neonates, and their roles of mycotic pneumonia in the morbidity and mortality of neonates in two NICUs in the Al-Ramadi Teaching Hospital for Maternity and Children.</p></div><div><h3>Methods</h3><p>All infants in this study had been diagnosed with septicemia and treated with empirical antimicrobial therapy. An early morning nasogastric tube (NG-tube) was used to collect swallowed sputum by suction for culture and sensitivity testing.</p></div><div><h3>Results</h3><p>The average white blood count for the neonates was 8547 ± 5884.5 cells/mm². The mean C-reactive protein was 39.3 ± 26 mg/l, the mean serum albumin was 2.9 ± 0.2 g/dl and the positive bacterial blood culture was 28 (46.7 %). 9 (15 %) neonates died during the study period. The NG-tube culture identified fungal growth in all samples. Of these, 49 (81.6 %) were identified as <em>Candida albicans</em>, 6 (10 %) as <em>Candida tropicalis</em>, and 5 (8.3 %) as <em>Cryptococcus laurentii</em>. The bacterial culture results from the NG-tube samples identified 13 (21.6 %) patients with gram-positive bacteria and 47 (78.3 %) with gram-negative bacteria.</p></div><div><h3>Conclusion</h3><p>We found a prevalence of Candida spp. among neonates in addition to microbial oxygen tube contamination, indicating a biosafety breach in the neonatal unit. Mycotic infection requires global attention as a probable cause of respiratory failure in neonatal septicemia.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":"6 3","pages":"Pages 137-141"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588933824000323/pdfft?md5=e98223a68d44a37acdaeed7c16c9e563&pid=1-s2.0-S2588933824000323-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541035","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":"Blockchain-based crowdsourcing for human intelligence tasks with dual fairness","authors":"Yihuai Liang ,&nbsp;Yan Li ,&nbsp;Byeong-Seok Shin","doi":"10.1016/j.bcra.2024.100213","DOIUrl":"10.1016/j.bcra.2024.100213","url":null,"abstract":"<div><div>Human intelligence tasks (HITs), such as labeling images for machine learning, are widely utilized for crowdsourcing human knowledge. Centralized crowdsourcing platforms face challenges of a single point of failure and a lack of service transparency. Existing blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust in evaluating the quality of workers' answers. We propose a blockchain-based crowdsourcing scheme for ensuring dual fairness (i.e., preventing false reporting and free riding) and improving on-chain efficiency concerning on-chain storage and smart contract computation. The proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee dual fairness. An efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic accumulators. This truth discovery scheme aims at inferring ground truth from workers' answers. The ground truth is further utilized to estimate the quality of workers' answers. Additionally, a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring truthfulness. The scheme has O(<em>n</em>) complexity for both on-chain storage and smart contract computation, regardless of the number of questions, where <em>n</em> denotes the number of workers. Formal security analysis is provided, and extensive experiments are conducted to evaluate its effectiveness and performance.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100213"},"PeriodicalIF":6.9,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701161","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":"A binary-domain recurrent-like architecture-based dynamic graph neural network","authors":"Zi-chao Chen,&nbsp;Sui Lin","doi":"10.1007/s43684-024-00067-9","DOIUrl":"10.1007/s43684-024-00067-9","url":null,"abstract":"<div><p>The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413589","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":"Structure-based discovery of F. religiosa phytochemicals as potential inhibitors against Monkeypox (mpox) viral protein","authors":"Ranjan K. Mohapatra ,&nbsp;Ahmed Mahal ,&nbsp;Pranab K. Mohapatra ,&nbsp;Ashish K. Sarangi ,&nbsp;Snehasish Mishra ,&nbsp;Meshari A. Alsuwat ,&nbsp;Nada N. Alshehri ,&nbsp;Sozan M. Abdelkhalig ,&nbsp;Mohammed Garout ,&nbsp;Mohammed Aljeldah ,&nbsp;Ahmad A. Alshehri ,&nbsp;Ahmed Saif ,&nbsp;Mohammed Abdulrahman Alshahrani ,&nbsp;Ali S. Alqahtani ,&nbsp;Yahya A. Almutawif ,&nbsp;Hamza M.A. Eid ,&nbsp;Faisal M Albaqami ,&nbsp;Mohnad Abdalla ,&nbsp;Ali A. Rabaan","doi":"10.1016/j.jobb.2024.05.004","DOIUrl":"https://doi.org/10.1016/j.jobb.2024.05.004","url":null,"abstract":"<div><p>Outbreaks of Monkeypox (mpox) in over 100 non-endemic countries in 2022 represented a serious global health concern. Once a neglected disease, mpox has become a global public health issue. A42R profilin-like protein from mpox (PDB ID: 4QWO) represents a potential new lead for drug development and may interact with various synthetic and natural compounds. In this report, the interaction of A42R profilin-like protein with six phytochemicals found in the medicinal plant <em>Ficus religiosa</em> (abundant in India) was examined. Based on the predicted and compared protein–ligand binding energies, biological properties, IC₅₀ values and toxicity, two compounds, kaempferol (C-1) and piperine (C-4), were selected. ADMET characteristics and quantitative structure–activity relationship (QSAR) of these two compounds were determined, and molecular dynamics (MD) simulations were performed. <em>In silico</em> examination of the kaempferol (C-1) and piperine (C-4) interactions with A42R profilin-like protein gave best-pose ligand-binding energies of –6.98 and –5.57 kcal/mol, respectively. The predicted IC₅₀ of C-1 was 7.63 μM and 82 μM for C-4. Toxicity data indicated that kaempferol and piperine are non-mutagenic, and the QSAR data revealed that piperlongumine (5.92) and piperine (5.25) had higher log P values than the other compounds examined. MD simulations of A42R profilin-like protein in complex with C-1 and C-4 were performed to examine the stability of the ligand–protein interactions. As/C and C-4 showed the highest affinity and activities, they may be suitable lead candidates for developing mpox therapeutic drugs. This study should facilitate discovering and synthesizing innovative therapeutics to address other infectious diseases.</p></div>","PeriodicalId":52875,"journal":{"name":"Journal of Biosafety and Biosecurity","volume":"6 3","pages":"Pages 157-169"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258893382400030X/pdfft?md5=ec15123379db8c297e57ae0d9b373a79&pid=1-s2.0-S258893382400030X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594907","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":"Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction","authors":"Ao Xiao,&nbsp;Wei Yan,&nbsp;Xumei Zhang,&nbsp;Ying Liu,&nbsp;Hua Zhang,&nbsp;Qi Liu","doi":"10.1007/s43684-024-00072-y","DOIUrl":"10.1007/s43684-024-00072-y","url":null,"abstract":"<div><p>The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00072-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412924","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":"‘The trivial tickets build the trust’: a co-design approach to understanding security support interactions in a large university","authors":"Albesë Demjaha, David Pym, Tristan Caulfield, Simon Parkin","doi":"10.1093/cybsec/tyae007","DOIUrl":"https://doi.org/10.1093/cybsec/tyae007","url":null,"abstract":"Increasingly, organizations are acknowledging the importance of human factors in the management of security in workplaces. There are challenges in managing security infrastructures in which there may be centrally mandated and locally managed initiatives to promote secure behaviours. We apply a co-design methodology to harmonize employee behaviour and centralized security management in a large university. This involves iterative rounds of interviews connected by the co-design methodology: 14 employees working with high-value data with specific security needs; seven support staff across both local and central IT and IT-security support teams; and two senior security decision-makers in the organization. We find that employees prefer local support together with assurances that they are behaving securely, rather than precise instructions that lack local context. Trust in support teams that understand local needs also improves engagement, especially for employees who are unsure what to do. Policy is understood by employees through their interactions with support staff and when they see colleagues enacting secure behaviours in the workplace. The iterative co-design approach brings together the viewpoints of a range of employee groups and security decision-makers that capture key influences that drive secure working practices. We provide recommendations for improvements to workplace security, including recognizing that communication of the policy is as important as what is in the policy.","PeriodicalId":44310,"journal":{"name":"Journal of Cybersecurity","volume":"14 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving","authors":"Kang Yuan,&nbsp;Yanjun Huang,&nbsp;Lulu Guo,&nbsp;Hong Chen,&nbsp;Jie Chen","doi":"10.1007/s43684-024-00071-z","DOIUrl":"10.1007/s43684-024-00071-z","url":null,"abstract":"<div><p>Artificial intelligence empowers the rapid development of autonomous intelligent systems (AISs), but it still struggles to cope with open, complex, dynamic, and uncertain environments, limiting its large-scale industrial application. Reliable human feedback provides a mechanism for aligning machine behavior with human values and holds promise as a new paradigm for the evolution and enhancement of machine intelligence. This paper analyzes the engineering insights from ChatGPT and elaborates on the evolution from traditional feedback to human feedback. Then, a unified framework for self-evolving intelligent driving (ID) based on human feedback is proposed. Finally, an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00071-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348390","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":"Unstructured Big Data Threat Intelligence Parallel Mining Algorithm","authors":"Zhihua Li, Xinye Yu, Tao Wei, Junhao Qian","doi":"10.26599/bdma.2023.9020032","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020032","url":null,"abstract":"","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"89 16","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks","authors":"Selvam Saravanan, Uma Maheswari Balasubramanian","doi":"10.26599/bdma.2023.9020027","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020027","url":null,"abstract":"","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"45 28","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review","authors":"Abed Mutemi, F. Bação","doi":"10.26599/bdma.2023.9020023","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020023","url":null,"abstract":": The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"13 8","pages":""},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}