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

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).

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.

Macrophages play a central role in modulating different biological and physiological events. The behaviors and functions of macrophages may be regulated by a host of factors, including their viability, proliferation rate, and population density. Specifically, the population density of macrophages has been increasingly reported to be correlated with their activities. It is, however, still unclear if changes in macrophage population density will alter the biophysical attributes of these cells, notably their morphology. Herein, label-free phase-contrast microscopy is coupled with machine learning to interrogate the relationship between the population density and morphological features of macrophages. Through a systematic approach, variations in the morphological phenotypes of macrophages, which are dependent on their population density, are revealed. In parallel, through unsupervised clustering, the presence of single-cell morphological heterogeneity within each macrophage population and subpopulation is elucidated. Next, discriminative morphological attributes which can be leveraged to distinguish between macrophages from different groups are identified through feature scoring. Finally, high-performing explainable supervised machine learning algorithms that can be employed to predict the population density of macrophages based on their size and shape features are identified. This work is anticipated to offer a deeper understanding of the association between macrophage population density and morphologyas well as the potential use of morphological attributes as predictive metrics for analyzing cell populations.

Animal-Robot Interaction

The cover illustrates a gregarious locust interacting with a biomimetic agent inoculated with Beauveria bassiana on an robotic experimental platform, highlighting the dynamics of social immunity and pathogen information spread within the swarm, as explored through innovative biohybrid method of this study. More details can be found in article 2400763 by Donato Romano and Cesare Stefanini.

Siamese Network

This paper proposed a novel AI framework that enhances guidewire tip tracking in image-guided therapy for vascular diseases. Combining a Siamese network with attention mechanisms ensures robust tracking despite visual ambiguities and tissue deformations. Validated on clinical angiography sequences and robotic platforms, it improves diagnostic and therapeutic precision in endovascular interventions. More details can be found in the Research Article by Peng Qi and co-workers (DOI: 10.1002/aisy.202500425).

Bio-Intelligent Cyborg Insect

The cover image features a Bio-Intelligent Cyborg Insect (BCI) guided by non-invasive ultraviolet (UV) stimulation. A lightweight wireless backpack and UV helmet enable real-time feedback control based on the insect’s natural sensory perception, achieving autonomous navigation in complex environments. This work highlights the integration of biological intelligence with engineered systems for advanced biohybrid robotics. More details can be found in article 10.1002/aisy.202400838 by Keisuke Morishima and co-workers.

Self-adaptive, easy-to-control, and low-cost gripper devices are indispensable in manufacturing and agriculture. However, the existing soft grippers cannot provide high response speed and firm grasping. Inspired by the natural, active entanglement behaviors of animals and plants, a rapid, cilia-like soft gripper design is proposed for grasping various objects via envelopes formed by the self-entanglement of multiple hollowed silicone tubes. The basic entanglement unit comprises a hollow, soft silicone tube with an actuation wire inside, which leads to compression and entanglement by fixing the front of the tube and drawing the actuation wire. Using multiple entanglement units enables sufficient mechanical interlocking between deformed tubes and grasped objects, avoiding the reliance on contact force control. Experimental results demonstrate that the developed soft gripper, with a cost lower than one dollar, can complete adaptive grasping within 1 s. The grasping success rate can reach 100% in grasping common irregular-shaped daily objects within the effective grasping range of the entanglement units. The design paves the way for harnessing the potential of embodied intelligence in soft robots, enabling fast and universal grasping.