Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices hold promise in precision medicine, offering novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would intuitively provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The communication and energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices’ data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs, reinforced by the adaptability and resilience of Heterogeneous Graph Transformers (HGTs), excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.

中文翻译：

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图神经网络作为高错误原始数据上基于太赫兹的流引导纳米级定位的推动者


当代纳米技术和材料科学的研究进展植根于纳米器件作为一种多功能工具的出现，它协调传感、计算、无线通信、数据存储和能量收集。这些设备在精准医学领域前景广阔，为血液中的疾病诊断、治疗和监测提供了新的途径。确保对诊断感兴趣的事件进行精确定位，这是流引导体内纳米级定位概念的基础，将直观地为检测到的事件提供附加的诊断价值。纳米设备生成的原始数据对于这种定位至关重要，包括事件检测指示器和自纳米设备最后一次通过心脏以来经过的时间。纳米设备的通信和能量限制导致间歇性操作和不可靠的通信，从本质上影响了这些数据。这就需要对这些数据的特征进行全面建模。这些缺陷也对现有的流引导定位方法的可行性产生了深远的影响，这些方法不足以解决复杂的环境问题。我们的第一个贡献在于用于流引导定位的原始数据分析模型，剖析通信和能源能力如何影响纳米设备的数据输出。该模型充当了重要的桥梁，协调理想化假设与流引导定位的实际挑战。为了解决这些实际挑战，我们还将图神经网络（GNN）集成到流程引导的本地化范例中。 GNN 得到了异构图变换器 (HGT) 的适应性和弹性的增强，擅长捕获纳米设备感测事件定位所固有的复杂动态交互。我们的结果凸显了 GNN 的潜力，不仅可以提高定位精度，还可以扩大覆盖范围以涵盖整个血流。