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Leveraging blockchain and LLMs for patient–clinical trial matching 利用区块链和llm进行患者-临床试验匹配

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-27 DOI: 10.1016/j.array.2025.100657

Diana Hawashin , Khaled Salah , Raja Jayaraman , Samer Ellahham , Ibrar Yaqoob

Efficiently matching patients to clinical trials is essential for advancing medical research and ensuring reliable outcomes. However, current matching methods face several challenges. These include data integrity issues from tampered records, privacy risks caused by weak anonymization, and manual processes that delay recruitment. In addition, centralized systems lack transparency, expose sensitive patient data to security vulnerabilities, and suffer from single points of failure that reduce resilience and trust. In this paper, we propose a blockchain and Large Language Models (LLMs)-driven solution for secure, trustworthy, traceable, decentralized, and transparent patient–clinical trial matching. Blockchain ensures data integrity, security, and transparency by eliminating single points of failure and enabling tamper-proof records. LLMs enhance patient–trial matching by automating the interpretation of complex eligibility criteria, improving accuracy, and significantly reducing the time required for manual review. Our approach uses Ethereum-based smart contracts to automate workflows such as trial registration, eligibility assessment, and consent tracking. We fine-tune GPT-4, T5, and Gemini on synthetic data derived from real clinical trial records and employ majority voting to ensure consistent and unbiased eligibility decisions. A prototype Gradio interface was developed as a minimum viable product (MVP) to demonstrate seamless interaction between LLMs and smart contracts. Performance evaluation based on accuracy (0.800), precision (0.733), recall (1.000), and F1-score (0.846) demonstrates reliable eligibility prediction. Cost analysis confirms affordability, and security evaluation verifies resilience against known threats. Comparison with existing solutions highlights the framework’s advantages in transparency, trust, and automation. The smart contract code is publicly available on GitHub.

有效地将患者与临床试验相匹配，对于推进医学研究和确保可靠的结果至关重要。然而，目前的匹配方法面临着一些挑战。这些问题包括篡改记录造成的数据完整性问题、弱匿名化造成的隐私风险，以及延迟招聘的手动流程。此外，集中式系统缺乏透明度，将敏感的患者数据暴露给安全漏洞，并且存在单点故障，从而降低了弹性和信任。在本文中，我们提出了一个区块链和大型语言模型（llm）驱动的解决方案，用于安全、可信、可追溯、分散和透明的患者-临床试验匹配。区块链通过消除单点故障和启用防篡改记录来确保数据的完整性、安全性和透明性。llm通过自动化解释复杂的资格标准，提高准确性，并显着减少人工审查所需的时间，从而增强了患者-试验匹配。我们的方法使用基于以太坊的智能合约来自动化工作流程，如试验注册，资格评估和同意跟踪。我们根据来自真实临床试验记录的合成数据对GPT-4、T5和Gemini进行微调，并采用多数投票来确保一致和公正的资格决定。作为最小可行产品（MVP），开发了一个原型gradient接口，以演示llm和智能合约之间的无缝交互。基于正确率（0.800）、精密度（0.733）、召回率（1.000）和f1分数（0.846）的性能评价表明合格性预测是可靠的。成本分析确认可负担性，安全评估验证针对已知威胁的弹性。与现有解决方案的比较突出了该框架在透明度、信任和自动化方面的优势。智能合约代码在GitHub上公开可用。

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引用次数: 0

AgnoSVD: Dynamic resource allocation for serverless workloads using collaborative filtering AgnoSVD：使用协作过滤为无服务器工作负载进行动态资源分配

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-27 DOI: 10.1016/j.array.2025.100662

Md. Shariar Kabir, Muhammad Abdullah Adnan

In serverless computing, determining the optimal resource configurations for workloads poses significant challenges, particularly due to the cloud provider’s limited visibility into workload specifics. This complexity is amplified when dealing with diverse workloads that vary in their characteristics. In this paper, we present AgnoSVD, an approach for predicting the optimum resource configuration for an incoming workload using Singular Value Decomposition (SVD). The proposed model uses collaborative filtering to extract the latent factors of the workloads and resource profiles. Therefore, the model remains agnostic to the specific details of the functions and the resource configurations. We tested our approach on well-known serverless systems like AWS lambda and Apache OpenWhisk and evaluated the system using 99 functional workloads. These workloads encompass both individual functions and chains of functions, addressing a range of computational and learning problems. To validate the system’s ability to adapt to changes, we also evaluated our system using functions with different input parameter sizes. Our evaluation shows that the model reaches convergence within 2 feedback iterations and results in a 32.41% decrease in average cost and a 5.18% average speedup, outperforming other state-of-the-art approaches.

在无服务器计算中，确定工作负载的最佳资源配置带来了重大挑战，特别是由于云提供商对工作负载细节的可见性有限。在处理特征各异的各种工作负载时，这种复杂性会被放大。在本文中，我们提出了AgnoSVD，这是一种使用奇异值分解（SVD）预测传入工作负载的最佳资源配置的方法。该模型采用协同过滤的方法提取工作负载和资源配置文件的潜在因素。因此，模型对功能和资源配置的具体细节保持不可知。我们在知名的无服务器系统（如AWS lambda和Apache OpenWhisk）上测试了我们的方法，并使用99个功能工作负载对系统进行了评估。这些工作负载包括单个功能和功能链，解决一系列计算和学习问题。为了验证系统适应变化的能力，我们还使用具有不同输入参数大小的函数来评估我们的系统。我们的评估表明，该模型在2次反馈迭代内达到收敛，平均成本降低32.41%，平均加速提高5.18%，优于其他最先进的方法。

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引用次数: 0

Machine learning-based prediction of maternal continuum of care completion: Evidence from Bangladesh Demographic and Health Survey 2022 基于机器学习的孕产妇连续护理完成预测：来自孟加拉国2022年人口与健康调查的证据

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-26 DOI: 10.1016/j.array.2025.100666

Syed Toukir Ahmed Noor , Raisha Binte Islam , Samin Yeasar , Sazid Siddique

Despite decades of investments in maternal healthcare, the completion of the maternal continuum of care (CoC), defined as receiving at least four antenatal care visits with a medically trained provider, delivery by a skilled birth attendant at a health facility, and postnatal care within 48 h, remains low in Bangladesh. This study aimed to develop a machine learning-based prediction model to identify women most likely to complete the maternal CoC, using data from the 2022 Bangladesh Demographic and Health Survey. The analysis included 5,128 ever-married women aged 15–49 who had a live birth in the five years preceding the survey. After data pre-processing, including handling class imbalance through Synthetic Minority Oversampling Technique (SMOTE), 16 predictor variables were selected using the Boruta feature selection algorithm. Seven supervised machine learning algorithms were developed and evaluated, including Random Forest, XGBoost, support vector machines, logistic regression, and artificial neural networks. Among the 5,128 women, only 25.3 % completed the maternal CoC in Bangladesh. The random forest performed best with an accuracy of 82 %, precision of 81 %, recall of 71 %, F1-score of 0.758 and an AUC of 0.889. Feature importance and SHAP analysis identified wealth index, husband's and respondent's education, media exposure, and women's decision-making autonomy at household as the most influential predictors. Identifying women at high risk of dropout can enable healthcare providers to deliver targeted counseling and interventions. These findings offer valuable insights to inform data-driven policies and strategies to enhance maternal health service utilization and reduce preventable maternal morbidity and mortality in low-resource settings like Bangladesh.

尽管在孕产妇保健方面进行了数十年的投资，但在孟加拉国，孕产妇连续护理（CoC）的完成率仍然很低，其定义是由受过医学培训的提供者进行至少四次产前护理，在保健设施由熟练助产士接生，并在48小时内进行产后护理。本研究旨在利用2022年孟加拉国人口与健康调查的数据，开发一种基于机器学习的预测模型，以确定最有可能完成孕产妇CoC的妇女。该研究分析了5128名年龄在15岁至49岁之间的已婚女性，这些女性在调查前的五年内有过一次活产。数据预处理后，通过合成少数派过采样技术（Synthetic Minority Oversampling Technique， SMOTE）处理类失衡，使用Boruta特征选择算法选择16个预测变量。开发并评估了七种监督机器学习算法，包括随机森林、XGBoost、支持向量机、逻辑回归和人工神经网络。在孟加拉国的5128名妇女中，只有25.3%的人完成了孕产妇CoC。随机森林的准确率为82%，精密度为81%，召回率为71%，f1得分为0.758，AUC为0.889。特征重要性和SHAP分析发现，财富指数、丈夫和受访者的教育程度、媒体曝光率和女性在家庭中的决策自主权是最具影响力的预测因素。确定有辍学高风险的妇女可以使医疗保健提供者提供有针对性的咨询和干预措施。这些发现提供了宝贵的见解，为数据驱动的政策和战略提供信息，以便在孟加拉国等资源匮乏的环境中提高孕产妇保健服务的利用率，降低可预防的孕产妇发病率和死亡率。

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引用次数: 0

Research on different automatic segmentation methods for color cascading framework in detecting malaria infection 不同颜色级联框架自动分割方法在疟疾感染检测中的研究

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-23 DOI: 10.1016/j.array.2025.100658

Cucun Very Angkoso , Yonathan Ferry Hendrawan , Ari Kusumaningsih , Achmad Bauravindah , Rima Tri Wahyuningrum , Deshinta Arrova Dewi

Malaria remains a global health challenge due to its significant mortality and morbidity worldwide. Our study presents a comparative analysis of five automated segmentation models such as Fuzzy C-Means (FCM), K-Means, Gaussian Mixture Model (GMM), Entropy Filtering, and Watershed, for detecting malaria infection in microscopic blood smear images. The study uses dataset of 574 images, consisting of four Plasmodium species.

The study uses dataset of 574 images, comprising four Plasmodium species (P. falciparum, P. malariae, P. ovale, and P. vivax) across three developmental stages: trophozoites, schizonts, and gametocytes. Preprocessing techniques used is namely Color Cascading Method, including RGB normalization, gamma correction, noise reduction, exposure compensation, and edge enhancement, were employed to optimize image quality prior to segmentation. Experimental results demonstrate that FCM consistently outperforms other methods, achieving an average accuracy of 98.26 %, specificity of 97.91 %, and sensitivity of 98.61 %. The robustness of FCM in detecting faint and overlapping structures highlights its suitability for automated malaria diagnosis systems, particularly in resource-limited settings. While Entropy Filtering shows promise as an alternative, its inconsistent performance across different lifecycle stages limits its practical utility. The findings of this study provide a foundation for developing accurate and accessible automated diagnostic tools to enhance malaria detection and support global eradication efforts.

由于疟疾在世界范围内的死亡率和发病率很高，它仍然是一项全球健康挑战。本研究对模糊c均值（FCM）、k均值（K-Means）、高斯混合模型（GMM）、熵滤波（Entropy Filtering）和分水岭（Watershed）五种自动分割模型进行了比较分析，用于检测显微镜下血液涂片图像中的疟疾感染。该研究使用了574张图像的数据集，包括四种疟原虫。该研究使用了574张图像的数据集，包括四种疟原虫（恶性疟原虫、疟疾疟原虫、卵形疟原虫和间日疟原虫），它们跨越了三个发育阶段：滋养体、分裂体和配子体。使用的预处理技术即颜色级联法，包括RGB归一化、伽马校正、降噪、曝光补偿和边缘增强，在分割前优化图像质量。实验结果表明，FCM的平均准确率为98.26%，特异性为97.91%，灵敏度为98.61%，优于其他方法。FCM在检测微弱和重叠结构方面的鲁棒性突出了它对疟疾自动诊断系统的适用性，特别是在资源有限的环境中。虽然熵过滤作为一种替代方法很有希望，但它在不同生命周期阶段的不一致性能限制了它的实际应用。本研究结果为开发准确和可获得的自动化诊断工具提供了基础，以加强疟疾检测并支持全球根除工作。

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引用次数: 0

Green AI techniques for reducing energy consumption in AI systems 减少人工智能系统能耗的绿色人工智能技术

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-23 DOI: 10.1016/j.array.2025.100652

Sunawar Khan , Naila Sammar Naz , Tehseen Mazhar , Muhammad Usman Tariq , Tariq Shahzad , Sghaier Guizani , Habib Hamam

This systematic review synthesizes current evidence on energy-reduction techniques across algorithmic, hardware, and infrastructure layers of AI systems. Model compression and knowledge distillation (e.g., DistilBERT) deliver ∼60 % faster inference with ∼40 % fewer parameters while retaining ∼97 % of baseline performance. Low-precision computation (quantization) yields up to ∼50 % energy reductions, and architecture-level strategies—such as neural architecture search and depthwise-separable convolutions in MobileNetV2—significantly lower compute and memory demand. Specialized accelerators (TPUs) and neuromorphic hardware further improve efficiency, while data-center measures (advanced cooling, virtualization, renewable integration) reduce system-level consumption. For generative-AI workloads, distillation, quantization, efficient architectures, and accelerator-optimized inference remain the primary pathways to lowering both training and inference energy. Across studies, recurring gaps include inconsistent energy-metric reporting, limited standardized benchmarks, and a dominant focus on accuracy over efficiency. Regulatory progress is uneven: the EU has introduced stronger transparency requirements, whereas comparable obligations are not yet global. Review limitations include heterogeneous methodologies and incomplete transparency artifacts, which restrict cross-study comparability. Future research directions include algorithm–hardware co-design, neuromorphic methods, energy-harvesting AI devices, improved data-center operations, and explainable-AI tools to support reliable, energy-aware deployment at scale.

本系统综述综合了人工智能系统的算法、硬件和基础设施层的节能技术的现有证据。模型压缩和知识蒸馏（例如，蒸馏器）以减少~ 40%的参数提供了~ 60%的更快的推理，同时保留了~ 97%的基线性能。低精度计算（量化）可减少高达50%的能量，而架构级策略（如mobilenetv2中的神经架构搜索和深度可分离卷积）可显著降低计算和内存需求。专用加速器（tpu）和神经形态硬件进一步提高了效率，而数据中心措施（高级冷却、虚拟化、可再生集成）降低了系统级消耗。对于生成型人工智能工作负载，蒸馏、量化、高效架构和加速器优化推理仍然是降低训练和推理能量的主要途径。在研究中，反复出现的差距包括不一致的能源度量报告，有限的标准化基准，以及主要关注准确性而不是效率。监管方面的进展是不平衡的：欧盟已经引入了更强的透明度要求，而类似的义务还不是全球性的。审查的限制包括异构方法和不完全透明的工件，这限制了交叉研究的可比性。未来的研究方向包括算法-硬件协同设计、神经形态方法、能量收集人工智能设备、改进的数据中心操作和可解释的人工智能工具，以支持可靠的、能量感知的大规模部署。

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引用次数: 0

MambaSolar-Forcaster: A trustworthy photovoltaic ultra-short-term power forecasting method based on normalized optimization and multi-step forecasting mechanism MambaSolar-Forcaster：基于归一化优化和多步预测机制的可信赖的光伏超短期功率预测方法

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-20 DOI: 10.1016/j.array.2025.100640

Li Ding , Xinyu Hu , Jian Deng , Weihua Zhu , Jiaming Hu

Against the backdrop of the global energy structure shifting towards clean and low-carbon, the high proportion of photovoltaic grid integration poses challenges to the safe and stable operation of the power system. The intermittency and volatility of photovoltaic output make high-precision and trustworthy ultra-short-term power prediction a key link in ensuring grid safety. Although deep learning models have shown potential in this field, traditional models such as RNN and Transformer have limitations in long-range dependencies and computational efficiency, and the uncertainty of their prediction results may lead to security decision risks. Therefore, this article proposes a new photovoltaic ultra short term power prediction model called MambaSolar-Forcaster. The core innovation of this study lies in introducing the Mamba architecture, a new generation state space model, into this field for the first time. By utilizing its selective state space mechanism, it efficiently captures long-range dependencies in photovoltaic power sequences while maintaining linear computational complexity, and generates more trustworthy prediction intervals. In addition, we have designed a normalization and multi-step prediction mechanism for photovoltaic data characteristics, and constructed a trustworthy AI evaluation system that integrates point prediction and interval prediction. Then, experiments were conducted on the PVDAQ dataset, and the results showed that MambaSolar-Forcaster significantly outperformed the comparison model in terms of prediction accuracy and interval prediction reliability. Its more compact and reliable prediction interval can provide stronger support for safe scheduling decisions in the power grid.

在全球能源结构向清洁低碳转变的大背景下，光伏并网比例过高对电力系统的安全稳定运行提出了挑战。光伏发电的间歇性和波动性使得高精度、可信赖的超短期电力预测成为保障电网安全的关键环节。尽管深度学习模型在该领域显示出潜力，但传统模型如RNN和Transformer在远程依赖关系和计算效率方面存在局限性，并且其预测结果的不确定性可能导致安全决策风险。为此，本文提出了一种新的光伏超短期功率预测模型MambaSolar-Forcaster。本研究的核心创新点在于首次将新一代状态空间模型Mamba架构引入该领域。该算法利用其选择状态空间机制，在保持线性计算复杂度的同时，有效地捕获光伏电力序列中的长期依赖关系，生成更可靠的预测区间。此外，我们设计了光伏数据特征归一化多步预测机制，构建了集点预测和区间预测于一体的可信AI评价系统。然后在PVDAQ数据集上进行实验，结果表明MambaSolar-Forcaster在预测精度和区间预测可靠性方面显著优于对比模型。其预测区间更为紧凑、可靠，可为电网的安全调度决策提供更有力的支持。

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引用次数: 0

Smart grid privacy data encryption and sharing algorithm based on multi-key homomorphic encryption 基于多密钥同态加密的智能电网隐私数据加密与共享算法

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-20 DOI: 10.1016/j.array.2025.100655

Xuehai Chen , Yantong Lin , Zhimin Liang , Zhenmin He

To realize the effective planning and regulation of smart grids, it is necessary to ensure the smart grid private data sharing's security. A smart grid privacy data encryption and sharing algorithm based on multi-key homomorphic encryption is proposed. This algorithm is based on the smart meters' private data collected at the device layer, and encrypts the data by using the multi-key homomorphic encryption method of the key generation center. The computing layer interacts with smart meters within its coverage area through fog nodes. After the data collected from smart meters are authenticated and aggregated, the data is transmitted to the cloud storage layer for storage. The data stored in the cloud storage layer is encrypted by using multi - key homomorphic encryption methods and transmitted to the server. After decryption, the server can obtain the details of the private data of each subarea and realize the encryption and sharing of the privacy data of the smart grid. The test results show that the algorithm has good encryption performance, with encryption times all within 700 ms. The data decryption probability is above 99.22 %, and the communication overhead required for shared transmission is above 2000bit in all cases. The intrusion rate is within 0.3 %, ensuring the safe sharing of private data.

要实现智能电网的有效规划和监管，必须保证智能电网私有数据共享的安全性。提出了一种基于多密钥同态加密的智能电网隐私数据加密与共享算法。该算法以智能电表在设备层采集的私有数据为基础，采用密钥生成中心的多密钥同态加密方法对数据进行加密。计算层通过雾节点与其覆盖区域内的智能电表交互。智能电表采集的数据经过认证和聚合后，传输到云存储层进行存储。存储在云存储层的数据采用多密钥同态加密方法进行加密，传输到服务器端。解密后，服务器可以获得各子区域的隐私数据的详细信息，实现智能电网隐私数据的加密和共享。测试结果表明，该算法具有良好的加密性能，加密时间均在700ms以内。数据解密概率在99.22%以上，共享传输所需的通信开销在2000bit以上。入侵率在0.3%以内，保证隐私数据的安全共享。

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引用次数: 0

Classifying and mining design diagrams in source code repositories using transfer learning 使用迁移学习对源代码存储库中的设计图进行分类和挖掘

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-20 DOI: 10.1016/j.array.2025.100656

Sergio Rodríguez, Daniel Benavides , Héctor Cadavid , Wilmer Garzón

This paper reports on the implementation of a design diagram classifier using transfer learning and fine-tuning. For the study, we built a labeled data set with 5981 images identifying the following design diagram categories: none (no design diagram represented), activity diagram, sequence diagram, class diagram, component diagram, use case diagram, and cloud diagrams. We then used the dataset, transfer learning, and fine-tuning techniques to train a specialized DenseNet169 convolutional network, starting from a model pre-trained on ImageNet. The newly trained network achieved a prediction accuracy of 98.6% (

0.986 \pm 0.0017

) and an F1-score of 98.3% (

0.983 \pm 0.0025

). Repository mining techniques were then used to analyze 2,469,206 images, equivalent to 231 GB of data, obtained from 287,201 repositories. The analysis revealed that design diagrams are often outdated relative to the project’s evolution; on average, diagrams were last updated 554 days prior to the repository’s latest commit. This significant lag suggests that while diagrams are present, practitioners primarily rely on the source code as the living design artifact.

本文报道了一种基于迁移学习和微调的设计图分类器的实现。在这项研究中，我们建立了一个带有5981张图像的标记数据集，识别了以下设计图表类别：无（没有设计图表表示）、活动图、序列图、类图、组件图、用例图和云图。然后，我们使用数据集、迁移学习和微调技术来训练一个专门的DenseNet169卷积网络，从在ImageNet上预训练的模型开始。新训练的网络预测准确率为98.6%(0.986±0.0017)，f1评分为98.3%（0.983±0.0025）。然后使用存储库挖掘技术分析了从287,201个存储库中获得的2,469,206张图像，相当于231 GB的数据。分析表明，相对于项目的发展，设计图经常是过时的；平均而言，图的最后更新要比存储库的最新提交早554天。这种显著的滞后表明，当存在图时，实践者主要依赖源代码作为活的设计工件。

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引用次数: 0

Evaluating sustainability barriers for digital platform services supply chain: A study on strategic intervention through industry-academia collaboration 数字平台服务供应链可持续性障碍评估：产学合作战略干预研究

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-19 DOI: 10.1016/j.array.2025.100651

Sanchari Ghosh , Sandeep Mondal , Nishit Kumar Srivastava

Digital Platform Services Supply Chains (DPSSC) form the operational backbone of contemporary digital economies, yet their sustainability performance is shaped by interdependent technical, organisational and behavioural constraints that remain insufficiently examined. This study addresses this gap by developing unified framework based on DEMATEL-ANP (DANP) and NSGA-II to identify, prioritise and test 7 sustainability barriers spanning from B1-B7. Expert elicitation and DANP analyses reveal a stable causal structure, in which three upstream systemic barriers B1 (ESG standardisation for digital platforms), B3 (energy-intensive infrastructure) and B2 (sustainable tech-innovation by industry-academia collaboration) emerge as system drivers with together accounting for nearly 70 % of global priority weight, whereas B4 (curricular gaps), B5 (behavioural nudges), B6 (algorithmic ESG indicators), B7 (carbon accountability) function as dependent barriers with limited leverage in the absence of upstream correction. NSGA-II is applied to quantify trade-offs between emission reduction and implementation effort under a parameterised case setting informed by publicly disclosed sustainability data. The optimisation yields stable Pareto fronts across convergence, hypervolume, spacing, multi-seed and uncertainty diagnostics. Strategies emphasising B1-B3 deliver largest marginal mitigation benefits within the efficiency zone, while interventions centred on B4-B7 rapidly encounter diminishing returns. This integrated evidence demonstrates that DPSSC sustainability follows a two-stage intervention logic one by addressing upstream structural enablers, the other followed by scaling downstream behavioural, algorithmic and logistical measures once system-level constraints are resolved. Thus, the study provides a transparent and decision-relevant basis for prioritising sustainability actions, strengthening industry-academia engagement and aligning digital platform operations with United Nations SDG 4,7,9,11–13 targets.

数字平台服务供应链（DPSSC）是当代数字经济的运营支柱，但其可持续性表现受到相互依赖的技术、组织和行为约束的影响，而这些制约因素仍未得到充分研究。本研究通过开发基于DEMATEL-ANP （DANP）和NSGA-II的统一框架来识别、优先考虑和测试从b1到b7的7个可持续性障碍，从而解决了这一差距。专家启发和DANP分析揭示了一个稳定的因果结构，其中三个上游系统性障碍B1（数字平台的ESG标准化），B3（能源密集型基础设施）和B2（产学研合作的可持续技术创新）作为系统驱动因素出现，合计占全球优先权重的近70%，而B4（课程差距），B5（行为推动），B6（算法ESG指标），B7（碳问责制）在缺乏上游纠正的情况下作为依赖障碍发挥有限的杠杆作用。NSGA-II应用于在公开披露的可持续性数据的参数化案例设置下量化减排和实施努力之间的权衡。优化产生稳定的帕累托战线跨越收敛，超大容量，间距，多种子和不确定性诊断。强调B1-B3的战略在效率区内产生最大的边际缓解效益，而以B4-B7为中心的干预措施的收益迅速递减。这些综合证据表明，DPSSC的可持续性遵循两个阶段的干预逻辑，一个是解决上游结构因素，另一个是解决系统级约束后扩展下游行为、算法和后勤措施。因此，该研究为确定可持续发展行动的优先顺序、加强产学研合作以及将数字平台运营与联合国可持续发展目标4、7、9、11-13相一致提供了透明和决策相关的基础。

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引用次数: 0

Ret-UNet: Enhancing medical image segmentation with self-retention Ret-UNet：增强医学图像的自保留分割

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

Array

Pub Date : 2025-12-18 DOI: 10.1016/j.array.2025.100653

Tianjun Guo, Weixin Zhao, Jian Peng

Medical image segmentation has advanced significantly due to the integration of deep learning techniques, particularly convolutional neural networks (CNNs) like U-Net. However, CNNs often struggle to capture global spatial relationships, which are crucial for accurately segmenting complex anatomical structures. To address this limitation, we propose Ret-UNet, a novel architecture that enhances the traditional U-Net framework by incorporating the Self-Retention mechanism. Self-Retention introduces an explicit shape prior related to the Euclidean distance, which effectively encode global spatial relationships within the image. The Ret-UNet leverages both local feature extraction and global context awareness by incorporating Ret Blocks into the U-Net like architecture, leading to improved segmentation performance. Evaluations on ACDC, CAMUS and Synapse datasets demonstrate that Ret-UNet achieves superior segmentation accuracy and robustness, outperforming state-of-the-art models. The code is available at https://github.com/weirdgit/RetUNet.

由于深度学习技术的集成，特别是卷积神经网络（cnn），如U-Net，医学图像分割取得了显著进展。然而，cnn经常难以捕捉全局空间关系，这对于准确分割复杂的解剖结构至关重要。为了解决这一限制，我们提出了Ret-UNet，这是一种新的架构，通过结合自保留机制来增强传统的U-Net框架。自保留引入了与欧几里得距离相关的显式形状先验，有效地编码了图像中的全局空间关系。Ret- unet通过将Ret块合并到类似U-Net的架构中，利用了局部特征提取和全局上下文感知，从而提高了分割性能。对ACDC、CAMUS和Synapse数据集的评估表明，Ret-UNet实现了卓越的分割精度和鲁棒性，优于最先进的模型。代码可在https://github.com/weirdgit/RetUNet上获得。

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