BenchCouncil Transactions on Benchmarks, Standards and Evaluations最新文献

Performance comparison of permissioned and permissionless blockchain by varying workload transaction 通过不同的工作负载事务比较有权限和无权限区块链的性能

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100251

Madhav Ajwalia , Parth Shah

Blockchain technology has fueled exponential growth across various industries, including finance, supply chain management, and healthcare, enabling greater transparency in transaction management and supporting decentralized implementations. This paper presents a comprehensive performance analysis of permissioned and permissionless blockchain platforms, specifically Hyperledger Fabric and Ethereum. The study evaluates these platforms with varying transaction workloads (100 to 1000 transactions) with a consistent network. Our objective is to measure key performance metrics such as send rate, throughput, latency, resource utilization, and transaction success rate using established benchmarking tools and methodologies. The findings offer valuable insights into the comparative strengths, limitations, and optimal use cases of these blockchain platforms across different performance parameters. The results indicate that Hyperledger Fabric achieves, on average, 3.5–4.5 times higher throughput and 10–12 times lower latency than Ethereum, while consuming 2.5–3 times less memory across tested workloads. In contrast, Ethereum demonstrates a higher send rate and lower CPU demand in some operations. Overall, the study suggests that Hyperledger Fabric is better suited for enterprise applications that demand high scalability and performance.

区块链技术推动了各个行业（包括金融、供应链管理和医疗保健）的指数级增长，使事务管理更加透明，并支持分散式实现。本文对有许可和无许可的区块链平台，特别是Hyperledger Fabric和以太坊进行了全面的性能分析。该研究在一致的网络中对这些具有不同事务工作负载（100到1000个事务）的平台进行了评估。我们的目标是使用已建立的基准测试工具和方法度量关键性能指标，如发送速率、吞吐量、延迟、资源利用率和事务成功率。这些发现为这些区块链平台在不同性能参数下的比较优势、局限性和最佳用例提供了有价值的见解。结果表明，Hyperledger Fabric的吞吐量平均比以太坊高3.5-4.5倍，延迟低10-12倍，同时在测试的工作负载上消耗的内存少2.5-3倍。相比之下，以太坊在某些操作中显示出更高的发送速率和更低的CPU需求。总体而言，该研究表明，Hyperledger Fabric更适合需要高可扩展性和高性能的企业应用程序。

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引用次数: 0

An adaptive opposite slime mold feature selection algorithm for complex optimization problems 一种复杂优化问题的自适应对面黏菌特征选择算法

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100250

Elsayed Badr , Mostafa Abdullah Ibrahim , Diaa Salama , Mohammed ElAffendi , Abdelhamied A Ateya , Mohamed Hammad , Alaa Yassin

The slime mould algorithm (SMA) has recently emerged as a soaring metaheuristic strategy to function optimization problems due to its solid exploration-exploitation balance that enables it to converge efficiently towards high-quality solutions. In spite of its broader applications, however, there remain areas where the algorithm is constrained in diversified exploration and the scope of its exploitation mechanisms. In bridging these loopholes, this work introduces a new variant known as the adaptive opposition SMA (AOSMA). AOSMA involves an adaptive opposition-based learning (OBL) method, which learns online how to add opposition-based solutions at the iteration process to enhance exploration abilities and avoid premature convergence. The adaptive policy enables the algorithm to escape local optima more effectively by occasionally generating alternative candidate solutions. Additionally, for the sake of increased exploitation, AOSMA also incorporates a plan in which the randomly selected search agent is progressively replaced with the current best-performing agent during position updating. The replacement process increases the focus of the algorithm towards prospective regions of the search space, and thus it converges more quickly towards the global optimum. The implemented AOSMA was exhaustively validated with both qualitative and quantitative measures in terms of thirteen rigorously proven benchmark test functions involving a variety of unimodal, multimodal, and composite landscapes to test its optimization ability extensively. Comparative tests on a collection of state-of-the-art metaheuristic algorithms confirmed that AOSMA consistently produces higher or highly comparable performances across a variety of problem instances. The experimental results confirm the robustness, adaptability, and improved search ability of the algorithm, highlighting its potential as an efficient optimization method for complex real-world problems. With the efficient fusion of adaptive exploration and improved exploitation, AOSMA provides a vital contribution to the field of research into swarm intelligence and metaheuristic optimization.

黏菌算法（SMA）最近成为一种快速发展的元启发式策略，用于解决函数优化问题，因为它具有良好的探索-开发平衡，使其能够有效地收敛于高质量的解决方案。然而，尽管该算法的应用范围越来越广泛，但在探索的多样性和开发机制的范围方面仍然存在一些限制。为了弥补这些漏洞，这项工作引入了一种新的变体，称为自适应对抗SMA （AOSMA）。AOSMA涉及一种自适应基于对立的学习（OBL）方法，该方法在线学习如何在迭代过程中添加基于对立的解，以增强探索能力并避免过早收敛。自适应策略通过偶尔生成备选解，使算法能够更有效地逃避局部最优。此外，为了增加利用，AOSMA还包含了一个计划，其中随机选择的搜索代理在位置更新期间逐渐被当前表现最好的代理取代。替换过程增加了算法对搜索空间前景区域的关注，从而更快地收敛到全局最优。通过13个经过严格验证的基准测试函数，包括各种单峰、多峰和复合景观，对所实现的AOSMA进行了全面的定性和定量验证，以广泛测试其优化能力。对一系列最先进的元启发式算法进行比较测试，证实了AOSMA在各种问题实例中始终如一地产生更高或高度可比的性能。实验结果证实了该算法的鲁棒性、适应性和改进的搜索能力，突出了其作为复杂现实问题的有效优化方法的潜力。该算法将自适应探索和改进开发有效地融合在一起，为群体智能和元启发式优化的研究领域做出了重要贡献。

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引用次数: 0

Data-driven financial fraud detection using hybrid artificial and quantum intelligence 混合人工智能和量子智能的数据驱动金融欺诈检测

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100252

Md. Sobuj Mia , Sujit Roy , Md Amimul Ihsan , Sadek Hossain , Md. Khabir Uddin Ahamed

The unauthorized use of a cardholder’s financial data, resulting in significant losses to individuals and companies, is known as credit card fraud. The increasing frequency and complexity of such fraud in the digital era highlight the absolutely vital need for reliable and accurate detection systems. Under the specific challenge of extreme class imbalance, this work investigates the credit card fraud identification performance of several Machine Learning (ML), Deep Learning (DL) and Quantum Machine Learning (VQC) algorithms. The study uses a commonly used dataset consisting of 284,807 anonymized credit card transactions, of which only 492 (0.17%) are fraudulent. To solve the class imbalance, we produced synthetic samples of the minority class utilizing the SMOTE, thus raising model sensitivity. Moreover, we enhanced model performance by means of hyperparameter tuning applied with Grid Search, Random Search, and Keras Tuner. Combining deep learning-based feature extraction with ensemble learning approaches, together with effective data balancing and hyperparameter tuning, yields, according to the results, a very accurate and dependable credit card fraud detection system. The hybrid model that includes AutoEncoder for feature extraction, Bagging (Random Forest), and Boosting (XGBoost) was the best, with 100% accuracy. This shows that this integrated technique is better than others. This approach provides a sensible analysis for building robust, real-time fraud detection systems for practical financial applications.

未经授权使用持卡人的财务数据，给个人和公司造成重大损失，被称为信用卡欺诈。在数字时代，这种欺诈的频率和复杂性日益增加，这凸显了对可靠和准确的检测系统的绝对迫切需求。在极端阶级不平衡的特定挑战下，本工作研究了几种机器学习（ML），深度学习（DL）和量子机器学习（VQC）算法的信用卡欺诈识别性能。该研究使用了一个常用的数据集，其中包括284,807笔匿名信用卡交易，其中只有492笔（0.17%）是欺诈行为。为了解决类不平衡问题，我们利用SMOTE生成了少数类的合成样本，从而提高了模型的灵敏度。此外，我们通过使用网格搜索、随机搜索和Keras调谐器进行超参数调谐来增强模型性能。将基于深度学习的特征提取与集成学习方法相结合，再加上有效的数据平衡和超参数调优，根据结果，产生了一个非常准确和可靠的信用卡欺诈检测系统。混合模型包括用于特征提取的AutoEncoder， Bagging （Random Forest）和Boosting （XGBoost）是最好的，准确率为100%。这表明这种集成技术比其他技术更好。这种方法为构建稳健的实时欺诈检测系统提供了合理的分析，用于实际的金融应用。

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引用次数: 0

Benchmark-based prioritizing sustainable consumption and production practices for achieving SDG 12 in India: A multi-criteria decision-making approach 基于基准的优先考虑可持续消费和生产实践，以实现印度的可持续发展目标12：多标准决策方法

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100253

Neha Gupta , Srikant Gupta

This study prioritizes sustainable consumption and production (SCP) practices to advance SDG 12 in India by employing a hybrid Grey Delphi–Grey DEMATEL framework. Twelve SCP practices identified through a comprehensive literature review were assessed by ten sustainability experts, with Grey Delphi confirming their relevance and Grey DEMATEL mapping the causal structure and influence dynamics within the system. The results show that circular economy practices, multi-stakeholder partnerships, and life cycle assessment function as core driving practices that exert substantial influence on the broader SCP landscape, while sustainable supply chain management, consumption education, urban planning, and green procurement appear as dependent practices shaped by these drivers. By integrating expert judgment and uncertainty-aware analytical techniques, the study provides a structured and replicable decision-support approach that assists policymakers, industry stakeholders, and practitioners in prioritizing impactful SCP interventions tailored to India’s socio-economic context, thereby supporting more effective progress toward sustainable development.

本研究采用灰色德尔菲-灰色DEMATEL混合框架，优先考虑可持续消费和生产（SCP）实践，以推进印度的可持续发展目标12。10位可持续发展专家通过全面的文献综述对12个SCP实践进行了评估，灰色德尔菲确认了它们的相关性，灰色DEMATEL绘制了系统内的因果结构和影响动态。结果表明，循环经济实践、多方利益相关者伙伴关系和生命周期评估是核心驱动实践，对更广泛的SCP景观产生重大影响，而可持续供应链管理、消费教育、城市规划和绿色采购则是受这些驱动因素影响的依赖实践。通过整合专家判断和不确定性分析技术，该研究提供了一种结构化和可复制的决策支持方法，帮助政策制定者、行业利益相关者和从业者根据印度的社会经济背景确定有影响力的SCP干预措施的优先顺序，从而支持更有效地朝着可持续发展的方向迈进。

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引用次数: 0

“We don’t plagiarise, we parrot”: Cognitive load and ethical perceptions in higher education written assessment “我们不抄袭，我们鹦鹉学舌”：高等教育书面评估中的认知负荷和伦理观念

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100254

Ibnatul Jalilah Yusof, Zakiah Mohamad Ashari, Lukman Hakim Ismail, Mira Panadi

Generative artificial intelligence has reshaped written assessment in higher education and sharpened concerns about “parroting,” the undisclosed use of AI-generated text with minimal cognitive engagement. This study examines the cognitive and ethical mechanisms underlying parroting among undergraduates in one Malaysian research university. Drawing on Cognitive Load Theory and Dual-System Theory, parroting is conceptualised across three dimensions: intrinsic load, extraneous load, and ethical rationalisation. Survey responses from 211 students were analysed using Rasch measurement to evaluate item reliability, construct separation, differential item functioning (DIF) across academic fields and item hierarchies. Results indicate that items function equivalently for engineering, non-engineering, and science students, supporting the instrument’s fairness and stability. Overall, findings show that parroting is most strongly driven by extraneous pressures such as vague instructions and heavy workload, followed by intrinsic challenges related to writing confidence and conceptual understanding. Ethical rationalisation is endorsed least frequently but becomes more salient when institutional guidance on AI use is unclear. The study offers implications for pedagogy and policy, underscoring the need for explicit AI-use guidelines, improved task design, and learning environments that promote ethically responsible engagement with generative technologies.

生成式人工智能重塑了高等教育的书面评估，并加剧了人们对“鹦鹉学舌”的担忧，“鹦鹉学舌”是指未公开使用人工智能生成的文本，而认知参与程度最低。本研究探讨了马来西亚一所研究型大学本科生鹦鹉学舌的认知和伦理机制。借鉴认知负荷理论和双系统理论，从三个维度对鹦鹉学舌进行了概念化：内在负荷、外在负荷和伦理合理化。采用Rasch测量法对211名学生的问卷调查结果进行分析，以评估项目的可靠性、结构分离、不同学科和项目层次的差异项目功能（DIF）。结果表明，项目的功能等效的工程，非工程和理科生，支持仪器的公平性和稳定性。总的来说，研究结果表明，鹦鹉学舌最强烈的驱动因素是外部压力，如模糊的指令和繁重的工作量，其次是与写作信心和概念理解相关的内在挑战。伦理合理化得到认可的频率最低，但当有关人工智能使用的制度指导不明确时，它就会变得更加突出。该研究为教学和政策提供了启示，强调需要明确的人工智能使用指南、改进的任务设计和学习环境，以促进对生成技术的道德负责任的参与。

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引用次数: 0

Evaluating barriers to establish digital trust in industry 4.0 for supply chain resilience in the Indian manufacturing industry 评估印度制造业供应链弹性在工业4.0中建立数字信任的障碍

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100247

Vaibhav Sharma , Rajeev Agrawal , Anbesh Jamwal , Vijaya Kumar Manupati , Vikas Kumar

Recent developments in Industry 4.0 technologies have led the manufacturing industry to implement them in its supply chains. The current state of lack of trust in digital systems has made organizations eager to build resilient systems to cope with uncertain circumstances. However, the challenges with handling stakeholder data with transparency, visibility, and accountability still persist. This transition demands the establishment of digital trust for secure information sharing and mitigating risks related to cybersecurity, data privacy, and potential misuse. Through a systematic literature review, this study identifies 17 barriers to establishing digital trust and applies exploratory factor analysis to group them into key dimensions. Further, a case-based analysis in the emerging Indian manufacturing economy’s context employing Pythagorean Fuzzy Analytic Hierarchy Process-Decision-Making Trial and Evaluation Laboratory is conducted to prioritize these barriers and explore their interrelationships. The findings reveal that ‘Top management commitment’ and ‘Cybersecurity’ are the most influential barriers to be taken care of to promote collaboration and responsiveness in a digitally enabled supply chain environment. The study contributes by guiding practitioners and researchers working on the digital transformations for supply chains, highlighting digital trust as a foundational capability for achieving resiliency in Supply Chain 4.0. Being less explored in the field of supply chain digitalization, this study is a first step forward to explore digital trust in the Supply Chain 4.0 for resilience.

工业4.0技术的最新发展促使制造业在其供应链中实施这些技术。目前人们对数字系统缺乏信任，这使得组织迫切希望建立有弹性的系统来应对不确定的环境。然而，以透明、可见性和问责制处理涉众数据的挑战仍然存在。这种转变需要建立数字信任，以实现安全的信息共享，并降低与网络安全、数据隐私和潜在滥用相关的风险。通过系统的文献回顾，本研究确定了17个建立数字信任的障碍，并运用探索性因素分析将其划分为关键维度。此外，本文采用毕达哥拉斯模糊层次分析法-决策试验与评估实验室，在新兴印度制造业经济背景下进行了基于案例的分析，以确定这些障碍的优先级并探索其相互关系。调查结果显示，在数字化的供应链环境中，“高层管理承诺”和“网络安全”是促进协作和响应的最具影响力的障碍。该研究为从事供应链数字化转型的从业者和研究人员提供了指导，强调了数字信任是实现供应链4.0弹性的基础能力。在供应链数字化领域的探索较少，本研究是探索供应链4.0中数字信任对弹性的第一步。

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引用次数: 0

Expert consensus and reliability validation of the portfolio assessment guideline for Chinese practical writing: An empirical study based on fleiss’ kappa 中文应用文写作组合评估指南的专家共识与信度验证——基于fleiss kappa的实证研究

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100248

Ying Wang (王莹) , Ibnatul Jalilah Yusof

Purpose

Portfolio assessment has been increasingly recognized as an effective approach to fostering comprehensive writing ability. However, its application in Chinese practical writing remains limited. The lack of standardized evaluation criteria has hindered its reliability and broader implementation. This study aimed to systematically develop and validate a Portfolio Assessment Guideline for Chinese practical writing, focusing on inter-rater reliability and coverage across four core dimensions: content, logical structure, language, and format.

Methods

Five higher education experts with extensive experience in practical writing instruction and research independently rated the guideline and its scoring rubrics for two key genres (summary and official notice), and inter-rater agreement was assessed using Fleiss’ Kappa coefficients.

Findings

The Kappa values for six core modules ranged from 0.79 to 1.00, with an overall Kappa of 0.87 across 14 sub-dimensions, indicating “almost perfect” agreement. Genre-specific analysis showed high overall consistency for summary (κ=0.87) and official notice (κ=0.89), with the summary’s “logical structure” dimension achieving “substantial agreement” (κ=0.68). Based on expert feedback, descriptive indicators were refined without altering the core framework.

Value

The findings provide robust evidence for the psychometric quality of the guideline, supporting its potential application in higher education and professional training for enhancing Chinese practical writing abilities.

目的档案袋评估是培养学生综合写作能力的有效方法。然而，它在汉语实际写作中的应用仍然有限。缺乏标准化的评价标准阻碍了其可靠性和更广泛的实施。本研究旨在系统地开发和验证中文实用写作组合评估指南，重点关注评分者之间的可靠性和覆盖四个核心维度：内容、逻辑结构、语言和格式。方法5位在实际写作指导和研究方面具有丰富经验的高等教育专家对指南及其评分标准（摘要和正式通知）进行独立评分，并采用Fleiss Kappa系数对评分者之间的一致性进行评估。六个核心模块的Kappa值从0.79到1.00不等，14个子维度的总体Kappa值为0.87，表明“几乎完美”一致。体裁特异性分析显示，摘要（κ=0.87）和官方通知（κ=0.89）的总体一致性较高，摘要的“逻辑结构”维度达到“基本一致”（κ=0.68）。根据专家反馈，在不改变核心框架的情况下，对描述性指标进行了细化。研究结果为该指南的心理测量学质量提供了有力的证据，支持其在高等教育和专业培训中提高汉语实际写作能力的潜在应用。

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引用次数: 0

An evaluation framework for measuring prompt wise metrics for large language models in resource-constrained edge 资源受限边缘下大型语言模型的快速明智度量评估框架

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100249

Partha Pratim Ray, Mohan Pratap Pradhan

Existing challenges in deploying large language models (LLMs) on resource-constrained devices stem from limited CPU throughput, memory capacity, and power budgets. Motivated by the lack of edge-specific evaluation tools, we introduce LLMEvaluator, a framework that profiles quantized LLMs — Qwen2.5, Llama3.2, Smollm2, and Granite3 — on a Raspberry Pi 4B using a suite of core and derived metrics. Our contributions include (i) a unified taxonomy that integrates latency, throughput, power variation, memory stability, and thermal behavior; (ii) prompt-wise analyses across ten NLP tasks; and (iii) correlation studies guiding optimizations. Key results show that Qwen2.5 leads in energy efficiency and throughput with a 68.44 MB memory standard deviation; Granite3 excels in memory stability , minimal load overhead, and per-token latency; Smollm2 suffers the highest total duration, longest prompt overhead, and lowest power efficiency; and Llama3.2 balances latency, throughput (8.12 tokens/s), and energy per token with moderate power variability (1.05 W std dev). Correlation analysis reveals that reducing model load time yields the largest improvement in end-to-end latency (

r > 0.9

), and that throughput gains directly translate into energy savings (

r \approx - 0.81

). LLMEvaluator empowers selection and tuning of LLMs for low-power environments.

在资源受限的设备上部署大型语言模型（llm）的现有挑战源于有限的CPU吞吐量、内存容量和功耗预算。由于缺乏特定于边缘的评估工具，我们引入了LLMEvaluator，这是一个框架，可以使用一套核心和派生指标在Raspberry Pi 4B上对量化llm （Qwen2.5, Llama3.2， Smollm2和Granite3）进行分析。我们的贡献包括：(i)集成延迟、吞吐量、功率变化、内存稳定性和热行为的统一分类法；（ii）跨十个NLP任务的即时分析；（三）指导优化的相关性研究。关键结果表明，Qwen2.5在能效和吞吐量方面领先，内存标准差为68.44 MB；Granite3在内存稳定性、最小负载开销和每个令牌延迟方面表现出色；Smollm2的总持续时间最长，提示开销最长，功率效率最低；Llama3.2平衡了延迟、吞吐量（8.12令牌/秒）和每个令牌的能量，并具有中等的功率可变性（1.05 W / dev）。相关分析表明，减少模型加载时间可以最大程度地改善端到端延迟（r>0.9），而吞吐量的提高直接转化为能源的节省（r≈−0.81）。LLMEvaluator支持为低功耗环境选择和调优llm。

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引用次数: 0

US-China geopolitical tensions and Indian stock market dynamics: evidence from NARDL and wavelet coherence 中美地缘政治紧张局势与印度股市动态：来自NARDL和小波相干性的证据

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-12-01 DOI: 10.1016/j.tbench.2025.100255

Dr. Animesh Bhattacharjee , Suravi Deb , Dr. Joy Das

The geopolitical tension between China and the United States have increasingly shaped global financial markets; the exact impacts on emerging economies like India remain poorly explored. This study examines the impacts of changes in US-China tension on the Indian stock market based on the use of nonlinear Autoregressive Distributed Lag (NARDL) modeling and wavelet coherence analysis. With monthly observations and the newly developed U.S.-China Tension Index (UCT), the study finds asymmetric short-run effects: heightened tensions are likely to dampen sentiment and reduce returns, whereas reduced tensions offer limited relief. Interest rates are a key determinant in both the short run and long run, underscoring their inherent role in determining capital flows. Wavelet analysis captures a change in the nature of the relationship, from persistent co-movement in the early period to more prompt, temporary responses in subsequent years. These results underscore the growing significance of geopolitical attitudes to market action, especially for economies that are increasingly open to international capital flows.

The novelty of the paper arises from the application of a novel geopolitical risk metric (UCT) to an untapped economy (India) through a hybrid econometric-time-frequency method that captures hitherto unseen asymmetric and dynamic market responses.

中美之间的地缘政治紧张局势日益影响着全球金融市场；对印度等新兴经济体的确切影响尚不清楚。本研究基于非线性自回归分布滞后（NARDL）模型和小波相干分析，探讨了中美紧张局势的变化对印度股市的影响。通过月度观察和新开发的美中紧张指数（UCT），该研究发现了不对称的短期效应：紧张局势加剧可能会抑制情绪并降低回报，而紧张局势缓解则有限。利率在短期和长期都是一个关键的决定因素，强调了它们在决定资本流动方面的内在作用。小波分析捕捉到了这种关系本质上的变化，从早期持续的共同运动到随后几年更迅速、暂时的反应。这些结果突显了地缘政治态度对市场行为日益重要的影响，尤其是对那些日益向国际资本流动开放的经济体而言。本文的新颖之处在于，通过计量经济学-时间-频率混合方法，将一种新的地缘政治风险度量（UCT）应用于一个尚未开发的经济体（印度），该方法捕捉了迄今为止未见过的不对称和动态市场反应。

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引用次数: 0

Corrigendum to “A Framework for Evaluating Cultural Bias and Historical Misconceptions in LLM Outputs” [BenchCouncil Transactions on Benchmarks, Standards and Evaluations 5 (2025) 100235] “评估法学硕士产出中的文化偏见和历史误解的框架”的勘误表[BenchCouncil基准、标准和评估交易5 (2025)100235]

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Pub Date : 2025-09-01 DOI: 10.1016/j.tbench.2025.100238

Moon-Kuen Mak , Tiejian Luo

引用次数: 0