In this work, WO₃/TiO₂ core–shell (C-S) nanowires (NWs) were successfully synthesized by the coaxial electrospinning method and subsequent high-temperature calcination treatment. After some microscopic structural characterizations, although the prepared WO₃–TiO₂ and TiO₂–WO₃ C-S NWs displayed quite different surface morphologies, both of the shell coatings were uniform and their typical shell thicknesses were extremely close, with mean values of 22 and 20 nm, respectively. In gas sensing tests, WO₃/TiO₂ C-S NWs exhibited good selectivity towards triethylamine (TEA) without significant interfering gases. Compared with bare WO₃ and TiO₂ NWs, WO₃/TiO₂ C-S NWs showed better gas sensing performance. Specifically, the optimal operating temperature and response of TiO₂–WO₃ C-S NWs to 100 ppm TEA were 130 °C and 106, which were reduced by 70 °C and increased by 5.73 times compared to bare WO₃, respectively. Obviously, the C-S nanostructures contributed to improving the gas sensing performance of materials towards TEA. Finally, some hypothetical sensing mechanisms were proposed, which were expected to have important reference significance for the design of target products applied to TEA sensing.

Leveraging serverless computing for cloud-based machine learning services is on the rise, promising cost-efficiency and flexibility are crucial for ML applications relying on high-performance GPUs and substantial memory. However, despite modern serverless platforms handling diverse devices like GPUs seamlessly on a pay-as-you-go basis, a longstanding challenge remains: startup latency, a well-studied issue when serverless is CPU-centric. For example, initializing GPU apps with minor GPU models, like MobileNet, demands several seconds. For more intricate models such as GPT-2, startup latency can escalate to around 10 seconds, vastly overshadowing the short computation time for GPU-based inference. Prior solutions tailored for CPU serverless setups, like fork() and Checkpoint/Restore, cannot be directly and effectively applied due to differences between CPUs and GPUs. This paper presents gCROP (GPU Checkpoint/Restore made On-demand and Parallel), the first GPU runtime that achieves <100ms startup latency for GPU apps with up to 774 million parameters (3.1GB GPT-2-Large model). The key insight behind gCROP is to selectively restore essential states on demand and in parallel during boot from a prepared checkpoint image. To this end, gCROP first introduces a global service, GPU Restore Server, which can break the existing barrier between restore stages and achieve parallel restore. Besides, gCROP leverages both CPU and GPU page faults, and can on-demand restore both CPU and GPU data with profile-guided order to mitigate costs caused by faults. Moreover, gCROP designs a multi-checkpoint mechanism to increase the common contents among checkpoint images and utilizes deduplication to reduce storage costs. Implementation and evaluations on AMD GPUs show significant improvement in startup latency, 6.4x-24.7x compared with booting from scratch and 3.9x-23.5x over the state-of-the-art method (CRIU).

Introduction: Multi-agent AI systems are believed to bring significant improvements in digital health, but it also brings new and more serious ethical issues. Such systems distribute the decision-making process among multiple interacting agents, and this decentralized decision-making system has raised ethical concerns in the medical field. On the one hand, it continues the ethical issues of traditional AI tools; on the other hand, the interaction processes within complex systems have also brought about new dilemmas. This narrative review aims to synthesize the ethical issues related to multi-agent AI systems in healthcare presented and explore the corresponding mitigation strategies. Methods: The study outcomes were synthesized using a narrative approach. Relevant records were gathered through Boolean searches in databases such as PubMed, Scopus, and Web of Science. A total of 21 articles related to multi-agent AI, healthcare, and ethical issues are included in this review. Results: Seven key ethical challenges were identified: (1) compound opacity, where interacting AI agents create layers of inscrutable decision-making; (2) error propagation and attribution difficulties, complicating accountability for clinical harm; (3) increased clinician dependence and automation bias, leading to potential deskilling and overreliance; (4) erosion of human oversight, as multi-agent AI systems operate beyond effective human control; (5) privacy and data security risks, stemming from complex data flows among agents; (6) threats to patient autonomy and informed consent, due to opaque or paternalistic AI recommendations; and (7) contextual blindness, reflecting a loss of individualized patient understanding in modular AI workflows. Furthermore, this review also summarized solutions proposed in the existing literature for these ethical issues. Conclusions: Multi-agent AI systems intensify existing ethical concerns in healthcare by distributing decision-making and blurring responsibility. To mitigate these issues, recent research advocates for the development of adaptive governance models, clear accountability frameworks, human–AI collaboration structures that preserve clinician authority, enhanced systems for explainability, and privacy-centered designs. In order to successfully incorporate agentic AI into healthcare, it is essential to maintain transparency, protect patient rights, and ensure that human-centered values continue to guide clinical decision-making in an era dominated by autonomous, interacting AI systems.

In this study, a terahertz microfluidic multi-band sensor was designed. Unlike previous microfluidic absorption sensors that rely on dipole resonance, the proposed sensor uses a physical mechanism for absorption by exciting higher-order lattice resonances in microfluidic structures. With a Fabry–Perot cavity, the sensor can form an absorption peak with a high quality factor (Q) and narrow full width at half maximum (FWHM). A high Q value and a narrow FWHM are valuable in the field of sensing and provide strong support for high-precision sensing. On this basis, the sensing performance of the device was investigated. The simulation results clearly show that the absorption sensor has ultra-high sensitivity, which reaches 400 GHz/Refractive Index Unit (RIU). In addition, the sensor generates three absorption peaks, overcoming the limitations of a single frequency band in a composite resonance mode and multidimensional frequency response, which has potential application value in the field of volatile organic compound (VOC) sensing.