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Adherence to biosafety and biosecurity protocols: An assessment of biomedical and veterinary laboratories in Benin

Q1 Social Sciences

Journal of Biosafety and Biosecurity

Pub Date : 2024-12-17 DOI: 10.1016/j.jobb.2024.11.001

Philippe Sessou , Nestor Noudeke , Yves Eric Denon , Aretas Tonouhewa , Alban Zohoun , Nassirou Boukary , Désirée Metodakou , Brian Samuelson , Dana Hag Hamed , Jenny Zhao , Sumegha Asthana , Mohammed Abid , Erin M. Sorrell

Diagnostic laboratories play a crucial role in surveillance and epidemic preparedness. However, gaps in adherence to biosecurity and biosafety protocols in managing, isolating, storing, and disposing of infectious pathogens in these facilities pose a serious risk to the laboratory, local populations, and the surrounding environment. In this study, we evaluated adherence to biosecurity and biosafety protocols in 96 public and private biomedical and veterinary laboratories in Benin to identify and address such gaps. We followed a cross-sectional study design. We used the Biosafety and Biosecurity in Laboratory Tool to assess biomedical laboratories and the Laboratory Mapping Tool to assess veterinary laboratories. Data were collected using KoBoCollect and analyzed with R software version 4.0.5. Descriptive analysis, the chi-squared test, and logistic regression were used to analyze the data. The findings revealed that most laboratories did not comply with established guidelines. Of all laboratories surveyed, 42% presented a very high risk, 38% a high risk, 15% moderate risk, and 5% were low risk. Overall, 80% of biomedical laboratories and 100% of veterinary laboratories were rated high risk. To improve effective biosecurity practices in laboratories in Benin to an acceptable standard, it is essential to implement robust biosecurity and biosafety policies. Additionally, capacity-building sessions should be conducted for laboratory staff and managers to educate them on the latest biosecurity protocols.

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引用次数: 0

Coordinating REST interactions in service choreographies using blockchain

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-12 DOI: 10.1016/j.bcra.2024.100241

Francesco Donini , Alessandro Marcelletti , Andrea Morichetta , Andrea Polini

In Service Oriented Computing (SOC), different services interact and exchange information to reach specific objectives. To model interorganizational SOC systems, choreography modeling languages have emerged to represent the distributed coordination among the involved organizations. From the realization perspective, blockchain technology is emerging as a promising run-time supporting peer-to-peer communication technology without the need for a central coordinator, thanks to its intrinsic security, trust, and decentralization characteristics. However, while blockchain can bring many advantages, technological barriers still limit its adoption in organizations, due to the costly and time-consuming learning process. For this reason, we propose RESTChain, a framework that automatically enables the interactions that take place among the participants in a service choreography exploiting blockchain technology. Starting from a choreography specification, the framework provides a set of mediators and automatically generates a smart contract that coordinates the service interactions. The mediators are software components that are directly connected with the smart contracts and expose REpresentational State Transfer (REST) APIs in compliance with the role played by the organizations in the choreography. In this way, the services deployed by one organization can communicate with the services made available by another organization through the blockchain in a secure and transparent manner. The proposed approach has been implemented on the Layer 2 Polygon blockchain and validated in a market retail case study analyzing its efficiency in terms of time and cost.

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引用次数: 0

An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion 一种基于自动特征提取和自适应数据融合的表面粗糙度智能预测方法

自主智能系统(英文)

Pub Date : 2024-12-12 DOI: 10.1007/s43684-024-00083-9

Xun Zhang, Sibao Wang, Fangrui Gao, Hao Wang, Haoyu Wu, Ying Liu

Machining quality prediction based on cutting big data is the core focus of current developments in intelligent manufacturing. Presently, predictions of machining quality primarily rely on process and signal analyses. Process-based predictions are generally constrained to the development of rudimentary regression models. Signal-based predictions often require large amounts of data, multiple processing steps (such as noise reduction, principal component analysis, modulation, etc.), and have low prediction efficiency. In addition, the accuracy of the model depends on tedious manual parameter tuning. This paper proposes a convolutional neural network quality intelligent prediction model based on automatic feature extraction and adaptive data fusion (CNN-AFEADF). Firstly, by processing signals from multiple directions, time-frequency domain images with rich features can be obtained, which significantly benefit neural network learning. Secondly, the corresponding images in three directions are fused into one image by setting different fusion weight parameters. The optimal fusion weight parameters and window length are determined by the Particle Swarm Optimization algorithm (PSO). This data fusion method reduces training time by 16.74 times. Finally, the proposed method is verified by various experiments. This method can automatically identify sensitive data features through neural network fitting experiments and optimization, thereby eliminating the need for expert experience in determining the significance of data features. Based on this approach, the model achieves an average relative error of 2.95%, reducing the prediction error compared to traditional models. Furthermore, this method enhances the intelligent machining level.

基于切削大数据的加工质量预测是当前智能制造发展的核心方向。目前，加工质量的预测主要依赖于过程和信号分析。基于过程的预测通常局限于基本回归模型的发展。基于信号的预测往往需要大量的数据，多个处理步骤（如降噪、主成分分析、调制等），并且预测效率较低。此外，模型的准确性依赖于繁琐的手动参数调整。提出了一种基于自动特征提取和自适应数据融合的卷积神经网络质量智能预测模型（CNN-AFEADF）。首先，通过对来自多个方向的信号进行处理，可以获得特征丰富的时频域图像，这对神经网络的学习有很大的帮助。其次，通过设置不同的融合权值参数，将三个方向对应的图像融合为一幅图像；采用粒子群优化算法（PSO）确定最优融合权参数和窗口长度。这种数据融合方法将训练时间缩短了16.74倍。最后，通过各种实验验证了所提出的方法。该方法可以通过神经网络拟合实验和优化自动识别敏感数据特征，从而消除了确定数据特征重要性时需要专家经验的需要。基于该方法，模型的平均相对误差为2.95%，与传统模型相比，降低了预测误差。进一步提高了智能化加工水平。

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引用次数: 0

Safe motion planning and formation control of quadruped robots 四足机器人的安全运动规划与编队控制

自主智能系统(英文)

Pub Date : 2024-12-05 DOI: 10.1007/s43684-024-00084-8

Zongrui Ji, Yi Dong

This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems. First, in order to improve the efficiency and safety of quadruped robots navigating in complex environments, this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field. The framework is suitable for both static and slow dynamic obstacle environments, aiming to achieve multiple goals of obstacle avoidance, minimizing energy consumption, reducing impact, satisfying dynamic constraints, and ensuring trajectory smoothness. This approach differs in that it reduces energy consumption throughout the movement from a new perspective. Meanwhile, this method effectively reduces the impact of the ground on the robot, thus mitigating the damage to its structure. Second, we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control. Finally, the proposed algorithm is validated through both simulations and real-world scenarios testing.

介绍了一种四足机器人和多智能体系统的运动规划和协同编队控制方法。首先，为了提高四足机器人在复杂环境中导航的效率和安全性，本文提出了一种将四足机器人动力学模型与无欧几里得符号距离场的梯度优化避障策略相结合的规划方法。该框架既适用于静态障碍环境，也适用于慢速动态障碍环境，旨在实现避障、最小化能耗、减小冲击、满足动态约束和保证轨迹平滑的多重目标。这种方法的不同之处在于，它从一个新的角度减少了整个运动的能耗。同时，该方法有效地减少了地面对机器人的冲击，从而减轻了对机器人结构的破坏。其次，将动态控制障碍函数与虚拟leader-follower模型相结合，通过模型预测控制实现高效安全的编队控制。最后，通过仿真和实际场景测试对算法进行了验证。

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引用次数: 0

A review on deep anomaly detection in blockchain

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100227

Oussama Mounnan , Otman Manad , Larbi Boubchir , Abdelkrim El Mouatasim , Boubaker Daachi

The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy, security, and trustworthiness. However, the challenges of cyber-attacks represent a real threat to systems based on this technology. The resort to the systems of anomaly detection focused on deep learning, also called deep anomaly detection, is an appropriate and efficient means to tackle cyber-attacks on the blockchain. This paper provides an overview of the blockchain technology concept, including its characteristics, challenges and limitations, and its system taxonomy. Numerous blockchain cyber-attacks are discussed, such as 51% attacks, selfish mining attacks, double spending attacks, and Sybil attacks. Furthermore, we survey an overview of deep anomaly detection systems with their challenges and unresolved issues. In addition, this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems. Finally, we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning, generative learning, and hybrid learning—with other methods based on graphs, and we highlight the ability of the proposed approaches to perform real-time anomaly detection.

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引用次数: 0

Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies

Q1 Computer Science

Virtual Reality Intelligent Hardware

Pub Date : 2024-12-01 DOI: 10.1016/j.vrih.2024.08.001

Tian ZHENG , Xinheng WANG , Xiaolan PENG , Ning SU , Tianyi XU , Xurong XIE , Jin HUANG , Lun XIE , Feng TIAN

The global trend of population aging poses significant challenges to society and healthcare systems, particularly because of neurocognitive disorders (NCDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD). In this context, artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of NCDs. Multimodal contactless screening technologies, such as speech-language processing, computer vision, and virtual reality, offer efficient and convenient methods for disease diagnosis and progression tracking. This paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms, feature extraction, and modeling approaches. Additionally, the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are explored. By providing a comprehensive summary and refinement of the extant theories, methodologies, and applications, this study aims to facilitate an in-depth understanding of these technologies for researchers, both within and outside the field. To the best of our knowledge, this is the first survey to cover the use of speech-language processing, computer vision, and virtual reality technologies for the detection of NSDs.

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引用次数: 0

Blockchain-enabled secure and authentic Nash equilibrium strategies for heterogeneous networked hub of electric vehicle charging stations 电动汽车充电站异构网络集线器的区块链安全和真实纳什均衡策略

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100223

Desh Deepak Sharma , S.N. Singh , Jeremy Lin

In the networked enlarged electric vehicle (EV) charging infrastructures, the security and authenticity of the stakeholders involved in the EV energy market pool are prime important. This paper proposes an EV network hub (EVNH) comprising EVs, EV aggregators (EVAs), and charging nodes in the networked EV energy market pool. The various EVAs implement different heterogeneous blockchains. The EVNH facilitates blockchain-based secure and resilient energy trading under grid to vehicle and vehicle to grid systems. The paper emphasizes interoperability challenges involving different blockchains to communicate and transfer assets or data between them. We suggest secure and trustworthy energy trading across various EVAs using multiple EV tokens for EV energy trading through cross-chain communications. The EVAs consider a Nash equilibrium-seeking strategy to find the Nash equilibrium in the noncooperative game of EVAs. The effectiveness of the proposed EVNH is tested using MATLAB, Solidity, and Python software.

引用次数: 0

Navigating blockchain adoption: An examination of actor alignment with the Diffusion of Innovation principles

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100228

Shipra Chhina , Mehmood Chadhar , Selena Firmin , Arthur Tatnall

Blockchain technology has garnered substantial interest due to its capacity to transform numerous industries by amplifying transparency and bolstering security measures. Despite the increasing interest, there is a void in existing literature regarding the alignment of actors with the Diffusion of Innovation (DOI) principles in the context of blockchain adoption. This gap restricts comprehension of the factors influencing adoption. This research addresses this void by investigating how actors align with the DOI principles in making decisions about blockchain adoption. The DOI model is combined with the innovation translation concept derived from Actor-Network Theory (ANT) to explore these complex dynamics in more detail. The results indicate that the decision-making process for blockchain adoption corresponds to the knowledge, persuasion, and decision stages, mirroring the phases found in the innovation translation approach. This research offers theoretical insights and practical knowledge that can be beneficial to individuals and organisations looking to promote the successful implementation of blockchain technology.

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引用次数: 0

Previs-Real:Interactive virtual previsualization system for news shooting rehearsal and evaluation

Q1 Computer Science

Virtual Reality Intelligent Hardware

Pub Date : 2024-12-01 DOI: 10.1016/j.vrih.2024.12.001

Che Qu , Shaocong Wang , Chao Zhou , Tongchen Zhao , Rui Guo , Cheng Wa Wong , Chi Deng , Bin Ji , Yuhui Wen , Yuanchun Shi , Yong-Jin Liu

Background

In the demanding field of live news broadcasting, the intricate studio production procedures and tight schedules pose significant challenges for physical rehearsals by cameramen. This paper explores the design and implementation of a lightweight virtual news previsualization system, leveraging virtual production technology and interaction design methods to address the lack of fidelity in presentations and manipulations, and the quantitative feedback of rehearsal effects in previous virtual approaches.

Methods

Our system, Previs-Real, is informed by user investigation with professional cameramen and studio technicians, and adheres to principles of high fidelity, accurate replication of actual hardware operations, and real-time feedback on rehearsal results. The system's software and hardware development are implemented based on Unreal Engine and accompanying toolsets, incorporating cutting-edge modeling and camera calibration methods.

Results

We validated Previs-Real through a user study, demonstrating superior performance in previsualization shooting tasks using the virtual system compared to traditional camera setups. The findings, supported by both objective performance metrics and subjective responses, underline Previs-Real's effectiveness and potential in transforming news broadcasting rehearsals.

Conclusions

Previs-Real eliminates the requirement for complex equipment interconnections and team coordination inherent in a physical studio by implementing methodologies complying the above principles, objectively resulting in a lightweight design of applicable version of virtual news previsualization system. It offers a novel solution to the challenges in news studio previsualization by focusing on key operational features rather than full environment replication. This design approach is equally effective in the process of designing lightweight systems in other fields.

{"title":"Previs-Real:Interactive virtual previsualization system for news shooting rehearsal and evaluation","authors":"Che Qu , Shaocong Wang , Chao Zhou , Tongchen Zhao , Rui Guo , Cheng Wa Wong , Chi Deng , Bin Ji , Yuhui Wen , Yuanchun Shi , Yong-Jin Liu","doi":"10.1016/j.vrih.2024.12.001","DOIUrl":"10.1016/j.vrih.2024.12.001","url":null,"abstract":"<div><h3>Background</h3><div>In the demanding field of live news broadcasting, the intricate studio production procedures and tight schedules pose significant challenges for physical rehearsals by cameramen. This paper explores the design and implementation of a lightweight virtual news previsualization system, leveraging virtual production technology and interaction design methods to address the lack of fidelity in presentations and manipulations, and the quantitative feedback of rehearsal effects in previous virtual approaches.</div></div><div><h3>Methods</h3><div>Our system, Previs-Real, is informed by user investigation with professional cameramen and studio technicians, and adheres to principles of high fidelity, accurate replication of actual hardware operations, and real-time feedback on rehearsal results. The system's software and hardware development are implemented based on Unreal Engine and accompanying toolsets, incorporating cutting-edge modeling and camera calibration methods.</div></div><div><h3>Results</h3><div>We validated Previs-Real through a user study, demonstrating superior performance in previsualization shooting tasks using the virtual system compared to traditional camera setups. The findings, supported by both objective performance metrics and subjective responses, underline Previs-Real's effectiveness and potential in transforming news broadcasting rehearsals.</div></div><div><h3>Conclusions</h3><div>Previs-Real eliminates the requirement for complex equipment interconnections and team coordination inherent in a physical studio by implementing methodologies complying the above principles, objectively resulting in a lightweight design of applicable version of virtual news previsualization system. It offers a novel solution to the challenges in news studio previsualization by focusing on key operational features rather than full environment replication. This design approach is equally effective in the process of designing lightweight systems in other fields.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 6","pages":"Pages 527-549"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143315910","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}

引用次数: 0

Corrigendum to “Effectiveness of chemical inactivation of infectious liquid biological waste: A randomized sample study of research laboratories in Switzerland” [J. Biosaf. Biosecur. 6(1) (2024) 16–26]

Q1 Social Sciences

Journal of Biosafety and Biosecurity

Pub Date : 2024-12-01 DOI: 10.1016/j.jobb.2024.09.001

Alina C. Teuscher , Charlotte Ruhnau , Nicole Stöcklin , Fabienne Wichmann , Evelyn Ilg Hampe , Claudia Bagutti

引用次数: 0

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