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What are desirable biosecurity trainings for veterinary practitioners and farmers? 兽医从业人员和农民需要接受哪些生物安全培训？

Q1 Social Sciences

Journal of Biosafety and Biosecurity

Pub Date : 2025-06-01 DOI: 10.1016/j.jobb.2025.06.003

Anna Maria Iatrou , Blerta Mehmedi Kastrati , Rreze M. Gecaj , Georgios Batikas , Jarkko K. Niemi , Claude Saegerman , Alberto Oscar Allepuz , Wiebke Jansen , Nancy De Briyne , Daniele De Meneghi , Murat Yılmaz , Evelien Biebaut , Ramazan Yildiz , Marco De Nardi , Carla Correia-Gomes , Tarmo Niine

Effective biosecurity training is essential for disease prevention in livestock systems; however, substantial gaps persist. We combined an online survey (74 fully completed questionnaires; 267 views) with two World Café workshops (∼60 participants) to map the current provision, competence levels, and training needs across Europe. Key findings: (i) self-rated biosecurity knowledge differed markedly between stakeholder groups and veterinarians and other stakeholders reported median scores close to 80/100; (ii) more than three-quarters of cattle (77 %) and 70 % of swine veterinarians perceived a major gap in their ability to demonstrate the economic benefits of biosecurity to clients; (iii) 39 – 44 % of cattle and small-ruminant veterinarians reported inadequate mixed (theory + practice) training formats, and up to 50 % of poultry veterinarians identified deficits in communication and behavior-change skills; (iv) across all discussions, participants favored modular, blended delivery that couples concise e-learning with on-farm coaching, supported by externally audited certification and greater farmer co-design. Therefore, recommendations focus on developing species-specific, flexible modules that embed communication and cost-benefit elements, provide micro-learning units for time-constrained farmers, and operate within a tiered certification framework linked to continuing professional development. Implementing these measures will narrow competence gaps, strengthen veterinarian–farmer engagement, and enhance disease preparedness throughout European livestock production.

有效的生物安全培训对牲畜系统的疾病预防至关重要；然而，巨大的差距仍然存在。我们结合了在线调查(74份完整填写的问卷；267个视图)与两个世界咖啡研讨会（约60名参与者）一起绘制欧洲目前的供应、能力水平和培训需求。主要发现：(i)利益相关者群体之间自评生物安全知识差异显著，兽医和其他利益相关者报告的中位数得分接近80/100；（ii）超过四分之三的牛兽医（77%）和70%的猪兽医认为，他们在向客户证明生物安全的经济效益方面存在重大差距；（iii） 39% - 44%的牛和小反刍动物兽医报告说，混合（理论+实践）培训形式不足，高达50%的家禽兽医发现在沟通和行为改变技能方面存在缺陷；（iv）在所有的讨论中，参与者都倾向于模块化、混合式交付，将简洁的电子学习与农场指导结合起来，并辅以外部审计认证和更多的农民共同设计。因此，建议的重点是开发特定品种的灵活模块，这些模块包含沟通和成本效益要素，为时间有限的农民提供微型学习单元，并在与持续专业发展相关的分层认证框架内运行。实施这些措施将缩小能力差距，加强兽医农民的参与，并在整个欧洲畜牧生产中加强疾病防范。

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引用次数: 0

DeFi risk assessment: MakerDAO loan portfolio case DeFi风险评估：MakerDAO贷款组合案例

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

Blockchain-Research and Applications

Pub Date : 2025-06-01 DOI: 10.1016/j.bcra.2024.100259

Ignat Melnikov , Irina Lebedeva , Artem Petrov , Yury Yanovich

Decentralized finance (DeFi) is a rapidly evolving blockchain technology that offers a new perspective on financial services through Web3 applications. DeFi offers developers the flexibility to create financial services using smart contracts, leading to a lack of standardized protocols and challenges in applying traditional finance models for risk assessment, especially in the early stages of adoption. The Maker protocol is a prominent DeFi platform known for its diverse functionalities, including loan services. This study focuses on analyzing the risk associated with Maker's loan portfolio by developing a risk model based on multiple Brownian motions and passage levels, with Brownian motions representing different collateral types and passage levels representing users' collateralization ratios. Through numerical experiments using artificial and real data, we evaluate the model's effectiveness in assessing risk within the loan portfolio. While our findings demonstrate the model's potential for assessing risk within a single DeFi project, it is important to acknowledge that the model's assumptions may not be fully applicable to real-world data. This research underscores the importance of developing project-specific risk assessment models for individual DeFi projects and encourages further exploration of other DeFi protocols.

去中心化金融（DeFi）是一种快速发展的区块链技术，它通过Web3应用程序为金融服务提供了新的视角。DeFi为开发人员提供了使用智能合约创建金融服务的灵活性，导致缺乏标准化协议，并且在应用传统金融模型进行风险评估方面存在挑战，特别是在采用的早期阶段。Maker协议是一个著名的DeFi平台，以其多种功能而闻名，包括贷款服务。本研究重点分析了Maker贷款组合的风险，建立了基于多个布朗运动和通道水平的风险模型，其中布朗运动代表不同的抵押品类型，通道水平代表用户的抵押比率。通过使用人工数据和真实数据的数值实验，我们评估了该模型在评估贷款组合风险方面的有效性。虽然我们的研究结果证明了该模型在单个DeFi项目中评估风险的潜力，但重要的是要承认该模型的假设可能并不完全适用于现实世界的数据。本研究强调了为单个DeFi项目开发特定于项目的风险评估模型的重要性，并鼓励进一步探索其他DeFi协议。

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引用次数: 0

Atomic and privacy-preserving cyclic cross-chain protocol based on chameleon hash function 基于变色龙哈希函数的原子和隐私保护循环交叉链协议

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

Blockchain-Research and Applications

Pub Date : 2025-06-01 DOI: 10.1016/j.bcra.2024.100262

Mengyan Li, Maoning Wang, Meijiao Duan

Blockchain-based digital assets have increasingly emerged in recent years, necessitating cross-chain swaps. Hash Time-Lock Contract (HTLC) is a widely used protocol for such swaps; however, simple hash time locks can allow attackers to analyze transaction paths, thereby causing privacy breaches and financial loss to users in some sensitive scenarios. To prevent payment path leakage, a privacy-preserving cyclic cross-chain protocol is proposed herein. This protocol primarily uses the Chameleon Hash (CH) protocol to obscure the correlation between users in the path, ensuring the privacy of cross-chain swaps. The protocol is divided into pre-swap, commit, and decommit phases. The pre-swap phase is firstly executed to determine the swap order. Then, users ensure atomicity via serial asset locking in the commit phase, and each receiver obtains swap assets from the corresponding sender via CH collision in the decommit phase. The security proof under the Universally Composable (UC) system demonstrates the correctness and usability of the protocol. In summary, the entire protocol ensures the atomicity and privacy of cross-chain swaps, providing a new principle and method to solve the privacy leakage problem caused by transaction path analysis.

近年来，基于区块链的数字资产越来越多，需要跨链交换。哈希时间锁合约（HTLC）是一种广泛使用的交换协议；但是，简单的散列时间锁可以允许攻击者分析事务路径，从而在某些敏感场景中给用户造成隐私泄露和经济损失。为了防止支付路径泄露，本文提出了一种保护隐私的循环跨链协议。该协议主要使用变色龙哈希（Chameleon Hash， CH）协议来模糊路径中用户之间的相关性，确保跨链交换的隐私性。该协议分为预交换、提交和解除提交阶段。首先执行预交换阶段以确定交换顺序。然后，用户在提交阶段通过串行资产锁定确保原子性，每个接收方在解提交阶段通过CH碰撞从相应的发送方获得交换资产。通用可组合（UC）系统下的安全性证明证明了该协议的正确性和可用性。综上所述，整个协议保证了跨链交换的原子性和隐私性，为解决交易路径分析带来的隐私泄露问题提供了新的原理和方法。

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引用次数: 0

Meta reinforcement learning based dynamic tuning for blockchain systems in diverse network environments 基于元强化学习的区块链系统在不同网络环境下的动态调谐

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

Blockchain-Research and Applications

Pub Date : 2025-06-01 DOI: 10.1016/j.bcra.2024.100261

Yue Pei , Mengxiao Zhu , Chen Zhu , Weihu Song , Yi Sun , Lei Li , Haogang Zhu

The evolution of blockchain technology across various areas has highlighted the importance of optimizing blockchain systems' performance, especially in fluctuating network bandwidth conditions. We observe that the performance of blockchain systems exhibits variations, and the optimal parameter configuration shifts accordingly when changes in network bandwidth occur. Current methods in blockchain optimization require establishing fixed mappings between various environments and their optimal parameters. However, this process exhibits poor sample efficiency and lacks the ability for fast adaptation to novel bandwidth environments. In this paper, we propose MetaTune, a meta-Reinforcement-Learning (meta-RL)-based dynamic tuning method for blockchain systems. MetaTune can quickly adapt to unknown bandwidth changes and automatically configure optimized parameters. Through empirical evaluations of a real-world blockchain system, ChainMaker, we demonstrate that MetaTune significantly reduces the training samples needed for generalization across different bandwidth environments compared to non-adaptive methods. Our findings suggest that MetaTune offers a promising approach for efficiently optimizing blockchain systems in dynamic network environments.

区块链技术在各个领域的发展突出了优化区块链系统性能的重要性，特别是在波动的网络带宽条件下。我们观察到区块链系统的性能表现出变化，当网络带宽发生变化时，最优参数配置也会发生相应的变化。目前区块链优化方法需要在各种环境及其最优参数之间建立固定的映射关系。然而，该方法的采样效率较差，缺乏对新带宽环境的快速适应能力。在本文中，我们提出了一种基于元强化学习（meta-RL）的区块链系统动态调谐方法MetaTune。MetaTune可以快速适应未知的带宽变化，自动配置优化参数。通过对现实世界的区块链系统ChainMaker的经验评估，我们证明了与非自适应方法相比，MetaTune显着减少了在不同带宽环境下泛化所需的训练样本。我们的研究结果表明，MetaTune为在动态网络环境中有效优化区块链系统提供了一种有前途的方法。

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引用次数: 0

SoK: On the security of non-fungible tokens SoK：关于不可替代代币的安全性

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

Blockchain-Research and Applications

Pub Date : 2025-06-01 DOI: 10.1016/j.bcra.2024.100268

Kai Ma , Jintao Huang , Ningyu He , Zhuo Wang , Haoyu Wang

Non-Fungible Tokens (NFTs) drive the prosperity of the Web3 ecosystem. By May 2024, the total market value of NFT projects reached approximately $69 billion. Accompanying the success of NFTs are various security issues, i.e., attacks and scams are prevalent in the ecosystem. While NFTs have attracted significant attention from both industry and academia, there is a lack of understanding of the kinds of NFT security issues. The discovery, in-depth analysis, and systematic categorization of these security issues are of significant importance for the prosperous development of the NFT ecosystem. To fill this gap, we perform a systematic literature review related to NFT security and identify 176 incidents from 248 security reports and 35 academic papers until May 1st, 2024. Through manual analysis of the compiled security incidents, we classify them into 12 major categories. Then, we explore potential solutions and mitigation strategies. Drawing from these analyses, we establish the first NFT security reference frame. In addition, we extract the characteristics of NFT security issues, i.e., the prevalence, severity, and intractability. We highlight the gap between industry and academia for NFT security and provide further research directions for the community. This paper, as the first Systematization of Knowledge (SoK) of NFT security, systematically explores security issues within the NFT ecosystem, shedding light on their root causes, real-world attacks, and potential ways to address them. Our findings will contribute to future research on NFT security.

不可替代代币（nft）推动了Web3生态系统的繁荣。到2024年5月，NFT项目的总市值达到约690亿美元。伴随着nft的成功而来的是各种安全问题，即攻击和诈骗在生态系统中普遍存在。虽然NFT已经引起了工业界和学术界的极大关注，但人们对NFT安全问题的种类缺乏了解。这些安全问题的发现、深入分析和系统分类对NFT生态系统的繁荣发展具有重要意义。为了填补这一空白，我们进行了与NFT安全相关的系统文献综述，并从248份安全报告和35篇学术论文中确定了176起事件，直至2024年5月1日。通过手工分析已编译的安全事件，我们将其分为12大类。然后，我们探讨了潜在的解决方案和缓解策略。根据这些分析，我们建立了第一个NFT安全参考框架。此外，我们还提取了NFT安全问题的特征，即普遍性、严重性和难治性。我们强调了工业界和学术界在NFT安全方面的差距，并为社区提供了进一步的研究方向。本文作为NFT安全的第一个系统化知识（SoK），系统地探讨了NFT生态系统中的安全问题，揭示了它们的根本原因、现实世界的攻击以及解决它们的潜在方法。我们的研究结果将有助于对NFT安全性的进一步研究。

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引用次数: 0

Advanced driver assistance system (ADAS) and machine learning (ML): The dynamic duo revolutionizing the automotive industry 高级驾驶辅助系统（ADAS）和机器学习（ML）：这两个动态组合将彻底改变汽车行业

Q1 Computer Science

Virtual Reality Intelligent Hardware

Pub Date : 2025-06-01 DOI: 10.1016/j.vrih.2025.01.002

Harsh SHAH , Karan SHAH , Kushagra DARJI , Adit SHAH , Manan SHAH

The advanced driver assistance system (ADAS) primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision, which leads to fewer fatal accidents and ensures higher safety. In the artificial Intelligence domain, machine learning (ML) was developed to make inferences with a degree of accuracy similar to that of humans; however, enormous amounts of data are required. Machine learning enhances the accuracy of the decisions taken by ADAS, by evaluating all the data received from various vehicle sensors. This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology. Initially, ADAS technology is introduced, along with its evolution, to understand the objectives of developing this technology. Subsequently, the critical algorithms used in ADAS technology, which include face detection, head-pose estimation, gaze estimation, and link detection are discussed. A further discussion follows on the impact of ML on each algorithm in different environments, leading to increased accuracy at the expense of additional computing, to increase efficiency. The aim of this study was to evaluate all the methods with or without ML for each algorithm.

先进的驾驶辅助系统（ADAS）主要是帮助驾驶员监控汽车的速度，帮助他们做出正确的决定，从而减少致命事故，确保更高的安全性。在人工智能领域，机器学习（ML）被开发出来，以类似于人类的精度进行推理；然而，需要大量的数据。通过评估从各种车辆传感器接收的所有数据，机器学习提高了ADAS做出决策的准确性。本研究总结了ADAS技术中使用的所有关键算法，并介绍了ADAS技术的发展。首先，介绍ADAS技术及其演变，以了解开发该技术的目标。随后，讨论了ADAS技术中使用的关键算法，包括人脸检测、头姿估计、凝视估计和链路检测。接下来将进一步讨论ML对不同环境中每种算法的影响，从而以额外的计算为代价提高准确性，从而提高效率。本研究的目的是评估每个算法使用或不使用ML的所有方法。

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引用次数: 0

Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks 异构信息网络中基于图神经网络的公共区块链交易链路预测方法

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

Blockchain-Research and Applications

Pub Date : 2025-06-01 DOI: 10.1016/j.bcra.2024.100265

Zening Zhao , Jinsong Wang , Jiajia Wei

Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve these problems, we extract transaction features to construct an Ethereum transaction heterogeneous information network (HIN) and propose a graph neural network (GNN)-based transaction prediction method for public blockchains in HINs, which can divide the network into subgraphs according to connectivity and increase the accuracy of the prediction results of transaction behavior. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with that of the search method. The accuracy of the proposed behavior prediction method also improves compared with that of the traditional random walk method, with an average accuracy of 83.82%.

Public区块链由于其匿名性，在交易隐私保护方面表现突出。数据的开放性为交易行为分析带来了可行性。目前，公链的交易数据庞大，交易对象和交易关系复杂。传统方法难以提取交易属性和预测交易行为。为了解决这些问题，我们提取交易特征，构建以太坊交易异构信息网络（HIN），并提出了一种基于图神经网络（GNN）的HIN中公链交易预测方法，该方法可以根据连通性将网络划分为子图，提高交易行为预测结果的准确性。实验表明，与搜索方法相比，所提出的事务子图划分方法的执行时间平均减少了70.61%。与传统的随机行走方法相比，所提出的行为预测方法的准确率也有所提高，平均准确率为83.82%。

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引用次数: 0

Human joint motion data capture and fusion based on wearable sensors 基于可穿戴传感器的人体关节运动数据采集与融合

自主智能系统(英文)

Pub Date : 2025-05-28 DOI: 10.1007/s43684-025-00098-w

Hua Wang

The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high latency. Consequently, in order to overcome the limitations of existing wearable sensors in data capture and fusion, the study initially constructed a model of the human joint and bone by combining the quaternion method and root bone human forward kinematics through mathematical modeling. Subsequently, the sensor data calibration was optimized, and the Madgwick algorithm was introduced to address the resulting issues. Finally, a novel human joint motion data capture and fusion model was proposed. The experimental results indicated that the maximum mean error and root mean square error of yaw angle of this new model were 1.21° and 1.17°, respectively. The mean error and root mean square error of pitch angle were maximum 1.24° and 1.19°, respectively. The maximum knee joint and elbow joint data capture errors were 3.8 and 6.1, respectively. The suggested approach, which offers a new path for technological advancement in this area, greatly enhances the precision and dependability of human motion capture, which has a broad variety of application possibilities.

人体运动数据捕获和融合领域具有广泛的潜在应用和市场机会。可穿戴传感器获取人体运动数据成本较低，比其他方法更方便，但也存在数据捕获精度差、延迟高的问题。因此，为了克服现有可穿戴传感器在数据采集和融合方面的局限性，本研究通过数学建模，将四元数法与根骨人体正运动学相结合，初步构建了人体关节和骨骼模型。随后，对传感器数据校准进行优化，并引入Madgwick算法来解决由此产生的问题。最后，提出了一种新的人体关节运动数据采集与融合模型。实验结果表明，该模型的横摆角最大平均误差和均方根误差分别为1.21°和1.17°。俯仰角的平均误差和均方根误差最大，分别为1.24°和1.19°。膝关节和肘关节数据捕获的最大误差分别为3.8和6.1。该方法极大地提高了人体运动捕捉的精度和可靠性，为该领域的技术进步提供了一条新的途径，具有广泛的应用前景。

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引用次数: 0

A blockchain-based collusion-resistant and traceable broadcast encryption scheme 一种基于区块链的抗合谋和可追踪的广播加密方案

IF 5.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2025-05-21 DOI: 10.1016/j.bcra.2025.100311

Tianqi Zhou , Kai Zhao , Wenying Zheng

Blockchain, as a rapidly developing technology nowadays, involves multi-party collaboration scenarios. However, as the number of users grows, security issues in blockchain systems also increase, driving the need for features such as collusion resistance and traceability. To meet the needs of multi-party collaboration on the blockchain, we propose a blockchain-based collusion-resistant and a traceable broadcast encryption scheme. On the one hand, the traitor tracing scheme is adopted to effectively enable accountability for malicious users. On the other hand, the SM2 public key encryption algorithm is deployed to satisfy high security requirements with relatively low computational costs. Security analysis demonstrates that the proposed scheme has the same level of security as the SM2 algorithm. Performance evaluation shows that the proposed scheme is superior to the relevant schemes and maintains functionalities such as collusion-resistant and traitor tracing.

区块链作为当今发展迅速的技术，涉及到多方协作场景。然而，随着用户数量的增长，区块链系统中的安全问题也在增加，从而推动了对抗串通和可追溯性等特性的需求。为了满足区块链上多方协作的需求，我们提出了一种基于区块链的抗合谋和可追踪广播加密方案。一方面，采用叛逆者追踪方案，有效实现对恶意用户的问责。另一方面，采用SM2公钥加密算法，以较低的计算成本满足较高的安全性要求。安全性分析表明，该方案具有与SM2算法相同的安全性。性能评估表明，该方案优于现有方案，并保持了抗合谋和叛逆者跟踪等功能。

引用次数: 0

Erratum to “A deep decentralized privacy-preservation framework for online social networks” 对“在线社交网络的深度去中心化隐私保护框架”的勘误

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

Blockchain-Research and Applications

Pub Date : 2025-05-19 DOI: 10.1016/j.bcra.2025.100299

Samuel Akwasi Frimpong , Mu Han , Emmanuel Kwame Effah , Joseph Kwame Adjei , Isaac Hanson , Percy Brown

引用次数: 0

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