题目：Vision-Language Model-Driven Human-Vehicle Interaction for Autonomous Driving: Status, Challenge, and Innovation（视觉语言模型驱动的自动驾驶人机交互：现状、挑战与创新）

作者：R. Zhao, A. Du, M. Cai, Z. Zhu and B. He

来源：Big Data Mining and Analytics（大数据挖掘与分析）, vol. 9, no. 2, pp. 425-447.

摘要：This paper investigates the potential of Vision-Language Models（VLMs） to enhance Human-Vehicle Interaction（HVI） in Autonomous Driving（AD） scenarios, particularly in interactions between vehicles and other traffic participants, with a focus on rationality and safety in external HVI. Leveraging recent advancements in large language models, VLMs demonstrate remarkable capabilities in understanding real-world contexts and generating significant interest in HVI applications. This paper provides an overview of AD, HVI, and VLMs, along with the historical context of large language model applications in HVI. The HVI discussed herein involves dynamic game processes encompassing perception and decision-making between vehicles and traffic participants, such as pedestrians. Furthermore, we examine the perceptual challenges associated with applying VLMs to HVI and compile relevant datasets. This research fills a gap in the existing literature by systematically analyzing the current status, challenges, and future opportunities of VLM applications in HVI. To advance VLM integration in AD, various implementation strategies are discussed. The findings highlight the potential of VLMs to transform HVI in AD, improving both passenger experience and driving safety. Overall, this study contributes to a comprehensive understanding of VLM applications in HVI and provides insights to guide future research and development.

编者译：本文探讨了视觉语言模型（VLMs）在自动驾驶场景中增强人车交互（HVI）的潜力，特别是车辆与其他交通参与者之间的互动中，外部HVI的合理性与安全性。得益于大语言模型的发展，VLMs在理解真实场景方面展现出卓越能力，引发了其在HVI应用中的广泛关注。本文围绕自动驾驶、人机交互与视觉语言模型展开系统综述，梳理了大语言模型在该领域应用的发展脉络。本文聚焦的HVI特指车辆与行人等交通参与者之间涉及感知与决策的动态博弈过程。进一步地，本文分析了将VLMs引入HVI任务时所面临的主要感知挑战，并对现有相关数据集进行了整理与评述。通过系统审视VLMs在HVI中的研究现状、技术瓶颈与发展机遇，填补了现有文献的空白。在此基础上，本文还探讨了推动VLMs深度融合至自动驾驶HVI的若干实施路径。研究结果表明，VLMs具备变革性潜力，能够显著提升自动驾驶环境下的HVI体验，兼顾出行舒适性与行车安全性。总体而言，本研究为全面理解VLMs在HVI领域的应用提供了系统视角，并为未来相关研究与实践提供了理论参考与方向指引。

题目：基于工作流的陆地生态系统碳循环实时同化预测系统

作者：万萌、何洪林、任小丽、聂宁明、曹荣强、王宗国、李凯、王晓光、王彦棡、王珏、高超

来源：数据与计算发展前沿, 2026, 8(1): 168-182.

摘要：本研究构建了一套陆地生态系统碳循环实时同化预测系统，包括数据采集、传输、分析、工作流、调度、预测和可视化等多个核心模块。通过结合深度学习气象模型、碳循环过程模型、数据同化算法和生态迭代预测方法，不断融合实时传输的站点观测数据，实现了台站碳汇的实时短期预测，为从观测到预测的野外站科研模式提供范例。自2023年2月部署以来，该系统已成功接入了鼎湖山、千烟洲、会同站等4个站点，迄今已积累超过11万条数据。系统显著提升了碳循环预测的实时性和效率，为生态研究和环境管理决策提供了可靠的数据支持和可观测的实时检索服务。

题目：A Deep Survival Model for Predicting Alzheimer's Diagnosis Based on Multi-Modal Longitudinal Data（基于多模态纵向数据预测阿尔茨海默病诊断的深度生存模型）

作者：B. K. Karaman, M. Nguyen, H. Kim and M. R. Sabuncu

来源：Big Data Mining and Analytics（大数据挖掘与分析）, vol. 9, no. 2, pp. 465-480.

摘要：In this study, we present a Transformer-based encoder model to predict Alzheimer's Disease (AD) progression from longitudinal multi-modal patient data. Our model, Longitudinal Survival Model for AD (LSM-AD), leverages rich temporal patterns present in sequences of patient visits, integrating multi-modal data, such as cognitive assessments and Magnetic Resonance Imaging (MRI) biomarkers to compute accurate diagnostic predictions. We conduct an empirical evaluation across two patient groups-Cognitively Normal (CN) individuals and those with Mild Cognitive Impairment (MCI)-tracking their progression for up to five follow-up years. Our results indicate that incorporating longer patient histories can yield superior performance compared to relying solely on a single visit, emphasizing the importance of historical context in improving predictive accuracy. Additionally, we show that the choice of the prediction head, training loss function and method for handling input missingness can significantly impact the quality of predictions. Notably, LSM-AD can improve Area Under the Receiver Operating Characteristic (AUROC) curve by up to 15% over previous state-of-the-art, when MRI biomarkers serve as the sole longitudinal feature. Our findings reinforce the value of multi-modal longitudinal data in evaluating patients, demonstrating its potential to improve early detection and monitoring of AD progression.

编者译：本研究提出一种基于Transformer的编码器模型，旨在利用纵向多模态患者数据预测阿尔茨海默病的进展。该模型，即阿尔茨海默病纵向生存模型（LSM-AD），通过挖掘患者多次随访序列中蕴含的丰富时序模式，整合认知评估与磁共振成像生物标志物等多模态数据，实现精准的诊断预测。本研究针对认知正常（CN）个体和轻度认知障碍（MCI）患者两类人群开展实证评估，追踪其在最多五年的随访期内的疾病进展轨迹。实验结果表明，与仅依赖单次访视数据相比，纳入更长周期的患者病史可获得更优的预测性能，可见历史时序信息对提升预测精度非常重要。此外，研究发现预测头结构、训练损失函数以及缺失值处理策略的选择均对预测质量具有显著影响。值得注意的是，当仅以MRI生物标志物作为纵向特征输入时，LSM-AD相较于现有最优模型，可将接收者操作特征曲线下面积（AUROC）提升高达15%。本研究进一步证实了多模态纵向数据在患者评估中的重要价值，展示了其在改善阿尔茨海默病早期识别与病程监测方面的广阔应用前景。

题目：农业科学数据自动挖掘框架设计与实践

作者：蓝晨阳、路长发、朱小杰、段军磊、任浩

来源：数据与计算发展前沿, 2026, 8(1): 119-128.

摘要：本文提出一种农业科学数据自动挖掘框架，通过智能化流水线架构PiFlow解决数据处理与应用场景的动态适配问题。融合流式处理引擎与有向无环图（DAG）任务编排技术，构建支持流批一体计算的异构数据流水线；采用模块化服务设计与容器化动态扩展机制，建立统一接口规范的组件抽象层，集成通用数据处理模块与领域专用挖掘工具；结合可视化交互引擎与预置算法模板库，实现复杂分析流程的低代码化构建。最后，基于六层子系统架构（处理流水线、执行引擎、调度、监控、日志及可视化引擎）构建原型系统。经作物基因组选择、耕地资源评价等场景验证，显著提升了多维度农业数据分析效率与跨场景复用性，为精准农业决策提供了可扩展的技术支撑。