Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献

Pneumatic Actuators

A wearable device using pneumatic actuators and origami pumps was proposed to generate multiple feedbacks including normal force, shear force, and vibration. The actuators and origami pumps are made of soft materials to reduce weight significantly, thereby increasing wearability while delivering appropriate force and air pressure. More details can be found in the Research Article by Youngsu Cha and co-workers (DOI: 10.1002/aisy.202500374).

Microrobots

Magnetic microrobots hold a promising future in many industries but are challenging to fabricate. An automated fabrication process is vital for accurate, reproducible products that can be developed quickly and efficiently. Grippers are one of the most widely used microrobots capable of numerous tasks such as grasping, crawling and targeted delivery. This cover is designed to represent the automated fabrication of magnetic microdevices. More details can be found in the Research Article by Onaizah Onaizah and co-workers (DOI: 10.1002/aisy.202500051).

Smart Prosthetic Sockets

Residual limb volume fluctuations can significantly compromise prosthetic socket fit, causing discomfort and instability, and often requiring frequent adjustments or replacements. This study presents a transfemoral socket with a motorized cable-driven mechanism and sensorized liner, adapting to volume changes via interface pressure monitoring. Controlled in open- or closed-loop modes through a custom mobile app, it enhances comfort, stability, and user autonomy. More details can be found in the Research Article by Linda Paternò and co-workers (DOI: 10.1002/aisy.202400995).

Magnetically Actuated Artificial Cilia

A magnetically actuated artificial cilia (MAAC) platform is developed for reversible microflow switching and particle manipulation. By modulating the beating frequencies via external magnetic fields, the system enables shear-driven particle trapping, directional release, and in situ microfluidic mixing. This strategy offers a promising, adaptive and versatile alternative for superior microscale flow control in robotics, biological processing, and lab-on-a-chip technologies. More details can be found in the Research Article by Chia-Yuan Chen and co-workers (DOI: 10.1002/aisy.202500431).

Soft Electronics

A Lorentz-force-driven, liquid metal–embedded surface delivers rapid, reversible 3D shape morphing for precise low-velocity flow control. With minimal power, it modulates near-wall flows in real time, offering versatile, programmable actuation for small UAVs, bio-inspired aerodynamics, and environmental sensing—bridging soft electronics with advanced fluid dynamics. More details can be found in the Research Article by Donghyun You, Leonardo P. Chamorro, Xinchen Ni, John A. Rogers, and co-workers (DOI: 10.1002/aisy.202500457).

Soft Wearable Glove

A soft wearable modular assistive glove, driven by miniature foldable pouch motor unit (MFPMU), is developed for hand rehabilitation and daily assistance. The MFPMU combine bending and elongation to naturally adapt to finger joint motion, achieving a fingertip force of 2.34 N at 50 kPa. The actuators generate a maximum torque of 240 mN·m and can be replaced within 30 seconds, offering high adaptability and user convenience. More details can be found in the Research Article by Jianbin Liu and co-workers (Doi: 10.1002/aisy.202500274).

The increasing demands of data-centric applications continue to expose the fundamental bandwidth bottleneck of the von Neumann architecture, where memory and computation are separated. Analog processing-in-memory (PIM) offers a promising pathway to overcome this limitation, and charge-trap flash (CTF) synapses stand out as attractive candidates owing to their scalability, multilevel storage, and complementary metal-oxide-semiconductor (CMOS) compatibility. In this work, a hardware-level analog PIM system is presented that integrates CTF synapse arrays, CMOS-based wordline drivers, and a novel successive integration-and-rescaling (SIR) neuron circuit in a chip-on-board configuration. Unlike conventional neuron designs that rely heavily on analog-to-digital conversion, the proposed SIR neuron performs bit-sliced accumulation entirely in the analog domain. This architecture not only minimizes analog-to-digital converter overhead but also achieves excellent linearity through input-node stabilization and functional capacitor separation, thereby enhancing both computational accuracy and area efficiency. The fabricated system is validated through handwritten digit classification on the modified national institute of standards and technology dataset, achieving an accuracy of 72.93%, which is only 3.91 percentage points lower than the software baseline under identical precision. These results underscore the pivotal role of the SIR neuron in bridging device-level innovations with system-level integration, positioning CTF-based analog PIM as a scalable and energy-efficient platform for neuromorphic computing.

Soft robots with multimode locomotion possess great application potential across various engineering fields due to their exceptional motion flexibility and environmental adaptability. Conventional approaches achieve multiple locomotion modes by designing a primary structure capable of different movements and then employing a series of actuators to drive each motion. Alternatively, soft robots made of stimuli-responsive materials usually take the shape of a thin sheet and generate different motions by modulating the external stimuli. This study presents a multimode soft robot based on a single braided tube composed of shape memory alloy wires set at distinct initial configurations. By strategically actuating different wires, the braided tube realizes axial contraction, elongation, and bending. A theoretical model is developed to analyze the underlying deformation mechanisms and to establish a quantitative relationship between the design parameters and the deformation, which is validated by experiments. Building on this, three types of braided soft robots, a crawling robot, a rolling robot, and a multimode robot capable of straight crawling, left/right turning, inchworm crawling, and rolling, are designed and actuated without additional actuators. The proposed structure–actuation integrated design approach provides a new way of developing highly integrated, multifunctional soft robots with enhanced adaptability and performance.

Accurate single-cell analysis is critical for diagnostics, immunomonitoring, and cell therapy, but coincident events, where multiple cells overlap in a sensing zone, can severely compromise signal fidelity. A hybrid framework combining a fully convolutional neural network (FCN) with compressive sensing (CS) to disentangle such overlapping events in 1D sensor data is presented. The FCN, trained on bead-derived datasets, accurately estimates coincident event counts and generalizes to immunomagnetically labeled CD4⁺ and CD14⁺ cells in whole blood without retraining. Using this count, the CS module reconstructs individual signal components with high fidelity, enabling precise recovery of single-cell features, including velocity, amplitude, and hydrodynamic diameter. Benchmarking against conventional state-machine algorithms shows superior performance, recovering up to 21% more events and improving classification accuracy beyond 97%. Explainability via class activation maps and parameterized Gaussian template fitting ensures transparency and clinical interpretability. Demonstrated with magnetic flow cytometry (MFC), the framework is compatible with other waveform-generating modalities, including impedance cytometry, nanopore, and resistive pulse sensing. This work lays the foundation for next-generation nonoptical single-cell sensing platforms that are automated, generalizable, and capable of resolving overlapping events, broadening the utility of cytometry in translational medicine and precision diagnostics, e.g., cell-interaction studies.

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests, and genomic profiling, which can be costly, complicated, and laborious. To overcome these challenges, RanBALL (an ensemble random projection-based model for identifying B-ALL subtypes) is presented, an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on >1700 B-ALL patients demonstrate that RanBALL achieves remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than t-SNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design is believed. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.