Patterning of organic semiconductors faces many technological challenges because organic semiconductor materials are highly sensitive to the fabrication environment, such as intensive irradiation and harsh chemical process during conventional lithography. Here, we demonstrate a process to structure organic semiconductors through aligned solution-stamping and subsequent pattern transfer. Arrays of organic thin film transistors were fabricated to demonstrate the potential of the process. During device fabrication, instantaneous spin-coating was used to deposit a polymer semiconductor onto polymer dielectrics from a solution of noncross solvent. The instantaneous spin-coating allows us to widen the scope of fabricating organic semiconductor devices, such as bottom-gate transistors with organic dielectrics and complementary circuits.
Disaster-prone areas require intelligent, real-time environmental monitoring systems to anticipate and mitigate ecological risks. This study introduces a novel optimization framework for flora-based monitoring using the Queen Honey Bee Migration (QHBM) algorithm in an Internet of Things system. This approach integrates edge computing, mobile-based image acquisition, and convolutional neural networks to emit vegetation health indicators as important proxies for predicting floods and landslides. The optimization problem is formulated as a multi-objective trade-off involving energy efficiency, latency, classification accuracy, and network diagnostics. QHBM, inspired by the migration and foraging behavior of honeybee colonies, is used to adaptively balance these objectives. The case study was conducted in the Greater Malang area, East Java, encompassing sub-watersheds prone to flooding and erosion. A real-world dataset, consisting of field-collected data representing the image acquisition frequency, communication interval, convolutional neural network model depth, and environmental sensor sampling rate, was obtained from multiple deployments across the region. System-level performance metrics, such as battery life, latency, and communication reliability, were evaluated through simulation using validated power consumption and communication models derived from the field data. Benchmarking results against Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm II indicate that QHBM provides better responsiveness, more optimal resource allocation, and higher decision accuracy. QHBM achieves a hypervolume of 0.712, outperforming Non-dominated Sorting Genetic Algorithm II (0.652) and Particle Swarm Optimization (0.601), with an energy efficiency of 0.86, an average latency of 1.45 s, a classification accuracy of 92.3%, and a packet loss rate of 3.1%. These findings confirm that QHBM is a promising optimization for proactive and sustainable disaster risk management.
Axial imaging is critical for high-speed volumetric multiphoton microscopy, particularly in neuroscience and functional imaging. This review systematically summarizes 3 major paradigms, single-point scanning, multifocus imaging, and continuous-beam strategies, highlighting their underlying mechanisms, performance trade-offs, and application scenarios. Projection-based and depth-encoded approaches are unified under a common framework of speed–resolution–complexity balance. Emerging encoding and compressive techniques further push volumetric imaging beyond conventional limits. Finally, future directions toward faster, deeper, and more quantitative imaging are discussed, emphasizing the integration of optical design and computational reconstruction.
With the continuous advancement of modern scientific experiments, electronic testing systems demand higher levels of automation and instrument collaboration. Traditional procedures rely heavily on manual configurations and tools like LabVIEW, which are inefficient and struggle with cross-device protocol compatibility. To address these challenges, this paper proposes a large language model (LLM)-based hierarchical multi-agent microelectronics testing architecture with highly autonomous and dynamic self-healing capabilities. The platform achieves adaptive control by decoupling natural language testing intents into task scheduling and code synthesis workflows. To overcome LLM hallucinations, we integrate a retrieval-augmented generation (RAG) mechanism utilizing local standard commands for programmable instruments (SCPI) knowledge graphs to enforce deterministic protocol constraints. Furthermore, a closed-loop iterative reflection engine captures underlying hardware anomalies to perform autonomous code reconstruction and self-correction. To ensure physical safety, a human-in-the-loop interactive review mechanism provides real-time visual feedback for pre-execution verification. Experimental results demonstrate that our closed-loop multi-agent system achieves a multi-device collaborative execution success rate of 82.3%. Additionally, the system reduces labor costs by over 85% and improves test development efficiency by 5 to 7 times compared to traditional manual programming. By overcoming the rigidness of traditional environments, the proposed platform exhibits superior flexibility, fault tolerance, and code maintainability, providing a new paradigm for intelligent test orchestration and laying a solid foundation for future cloud-based distributed laboratory infrastructures.
