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

Non-small cell lung cancer (NSCLC) comprises the largest subtype of lung cancer with the most cases. Lung adenocarcinoma and lung squamous cell carcinoma are two NSCLC subtypes that pose challenges for accurate diagnosis using conventional methods, including histological examination and imaging, which can be slow and inconclusive. To address these concerns, RPSLearner is proposed, which combines random projection (RP) for dimensionality reduction and stacking ensemble learning to accurately predict lung cancer subtypes. Specifically, multiple independent RP matrices are first generated to project the high-dimensional RNA-seq data into a lower-dimensional space, whose features are subsequently concatenated. After that, the concatenated RP features are fed into a stack of diverse base classifiers, and integrated the predictions from base models via a deep linear layer network. Benchmarking tests on 1 333 NSCLC patients demonstrated that RPSLearner outperformed state-of-the-art approaches for lung cancer subtype classification. Specifically, RPSLearner efficiently preserved sample-to-sample distances even after significant dimension reduction, and the meta-model in RPSLearner yielded consistently higher scores than individual base models. In addition, the feature fusion method outperformed conventional score ensemble methods. We believe RPSLearner is a promising model for downstream lung cancer clinical diagnosis, and it holds the potential to be extended to subtyping of other types of cancer.

Bats species navigating dense vegetation based on biosonar must obtain sensory information about their environments from “clutter echoes”, i.e., echoes that are superpositions from many unresolved reflecting facets (e.g., leaves) with unpredictable individual waveforms. Prior results suggested that pinna deformations can aid performance in sensing tasks based on deterministic echo patterns, raising the question of whether varying pinna shapes can also have functional significance for biosonar tasks performed on clutter echoes. To test this hypothesis, this work investigates whether different pinna shapes have a consistent effect on clutter echoes despite the random nature of these signals. This is accomplished using a dedicated laboratory setup that produces large amounts of uncorrelated clutter echo data by agitating artificial foliage with fans between echo recordings. Deep learning then identifies the pinna shape that receives a given clutter echo using a data-driven classification approach that learns features directly from echoes without explicit physical modeling. A ResNet-50 achieves 97.8% overall classification accuracy for the pinna shape conformations (true-positive identifications 91.67–100%), whereas a two-dimensional convolutional neural network operating on echo spectrograms still achieves 90% accuracy. These findings demonstrate that even small pinna deformations can impart consistent effects on the clutter echoes.

Inspired by organisms that utilize multimodal locomotion strategies to adapt to diverse environments, the development of analogous capabilities in soft robots has garnered growing attention. This review comprehensively surveys recent advances in multimodal locomotion within soft robotics. Typical locomotion modes are summarized and categorized. Furthermore, the underlying mechanisms enabling multimodal locomotion, encompassing both the integration of distinct locomotion modes and transitions between them, are discussed in detail and classified into three primary categories: active control-based, reconfiguration-based, and environment-responsive strategies. Leveraging these mechanisms, soft robots demonstrate enhanced adaptability for applications such as cross-domain transition, surface adaptation, and obstacle negotiation. Finally, key challenges in advancing the capabilities of multimodal locomotion to address real-world applications are discussed.

Perching robots offer an effective solution to the energy limitations of small robots during long-duration missions. However, without integrated sensing capabilities, they are prone to failure in complex environments. This study presents a remora-inspired sensing suction cup to enhance adhesion reliability for robots in complex aerial–aquatic conditions. Mimicking remora fish, the design incorporates liquid metal microchannel sensors arranged at 90° intervals to monitor lip deformation, enabling real-time assessment of adhesion state and force distribution. The optimized suction cup morphology improves deformation sensitivity, while the integrated sensor system operates effectively in both aquatic and aerial environments. Performance tests demonstrate that the sensors exhibit nonlinear but repeatable responses, with 2° bending resolution and stable operation over 1,000 cycles despite minor hysteresis. Experimental results confirm that the four-directional sensor array can reflect adhesion status and horizontal force detection, validating the design's feasibility. When deployed on an aerial–aquatic robot, the system successfully enables real-time leakage detection, lateral disturbance detection, and environmental tactile sensing. This bioinspired approach enhances the environmental adaptability and operational reliability of robots, offering a robust solution for maintaining attachment in complex conditions and significantly enhances the applicability of such systems in robotic applications.

Skeletal muscles are the primary power source for voluntary limb joint motions, thus muscle deformation (MD) is vital to reflect human motions. However, most sensors can capture only 1D MD features, and are suitable only for on-land scenarios, leading to the under evaluation and under exploitation of MD sensing. This article develops a 4 × 4 soft magnetic sensor array (SMSA) to capture 3D MD distribution. Compared to solid structures, the used porous elastomer mitigates hydraulic pressure disturbances by half within 0–100-m water depth, while sensitivity increases by 10 times. The SMSA has consistent amphibious measurements and about 200 ms faster response than inertial measurement units (IMUs). Mapping between 3D magnetic flux densities and deformations of elastomers is justified by calibration errors within 1% of full ranges. Experiments justify the proposed method in multiple environments, muscles, motions, and subjects. Average gait classification accuracy is 98.73%, and phase estimation error is 2.85% when using only one SMSA, which is better than existing commercial sensors (with 82.40% and 10.39% for one IMU, and 89.06% and 6.33% for one flexible resistive sensor array). The proposed method can contribute to muscle state monitoring for human–machine interaction, rehabilitation engineering, and sports science.

Understanding rare-earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional-materials fabrication—as a surrogate for studying mineralization with data-driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine-learning models—K-nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB-based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy-REE systems. Overall, the study shows that functional-materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less-studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

The potential application of phosphate semiconductor glass in the interlayer of resistive random access memory (RRAM) is investigated. Glasses based on (50–x)% V₂O₅−50% P₂O₅ are synthesized, which are doped with x% MO (where MO = ZnO, CaO, or Na₂O). X-ray diffraction analysis reveals that the ZnO and CaO series are amorphous, while the Na₂O series is crystalline. Differential scanning calorimetry analysis reveals that the glass transition temperature (T_g) is around 200 °C. X-ray photoelectron spectroscopy analysis reveals that the internal V elements are primarily +4 and +5. Initial electrical measurements indicate that the ZnO series glass exhibits semiconductor electrical properties. Additionally, nanodevices are fabricated and measured to demonstrate the resistive switching characteristics, with conduction mechanisms such as trap-assisted tunneling, space-charge limiting current, or Ohmic conduction. This study demonstrates the potential of phosphate semiconductor glass for application in RRAM and paves the way for the future development of all-glass RRAM components.

The postoperative rehabilitation of ankle fractures, particularly in the home setting, has a crucial influence on the recovery of lower limb function. To enhance the portability, real-time performance, and safety of postoperative remote rehabilitation training, this study proposes a novel robot-assisted remote rehabilitation system tailored for postoperative ankle fracture patients. Based on a distributed system architecture, the hardware system enables modular decomposition and facilitates wireless control of the lower controller. The total weight of the robotic system is 2.634 kg. By combining a deep learning algorithm with an interpolation fitting method, the time delay in interaction force signals during remote communication is predicted and compensated. The control frequency is elevated to 100 Hz with a maximum normalized root mean square error of 10.89%, ensuring the precision and continuity of the robot control system. Additionally, a full-cycle rehabilitation training strategy based on adaptive admittance control with system stiffness identification is proposed, encompassing passive, active–passive, isotonic, and active activities of daily living trainings. Experimental results indicate that the robotic system can execute the training strategies at each phase with high accuracy and safety, and the proposed adaptive control strategy has better compliance than fixed parameter admittance control and fuzzy admittance control methods.

Laser powder bed fusion (PBF-LB) is an additive manufacturing (AM) technology for producing complex geometry parts. However, the high cost of post-processing coarse as-built surfaces drives the need to control surface roughness during fabrication. Prior studies have evaluated the relationship between process parameters and as-built surface roughness, but they rely on forward models using trial-and-error, regression, and data-driven methods based only on areal surface roughness parameters that neglect spatial surface characteristics. In contrast, this study introduces, for the first time, an inverse data-centric framework that leverages machine learning algorithms and an experimental dataset of Inconel 718 as-built surfaces to predict the PBF-LB process parameters required to achieve a desired as-built roughness. This inverse model shows a prediction accuracy of ≈80%, compared to 90% for the corresponding forward model. Additionally, it incorporates deterministic surface roughness parameters, which capture both height and spatial information, and significantly improves prediction accuracy compared to only using areal parameters. The inverse model provides a digital tool to process engineers that enables control of surface roughness by tailoring process parameters. Hence, it establishes a foundation for integrating surface roughness control into the digital thread of AM, thereby reducing the need for post-processing and improving process efficiency.

Tensegrity Robotics

This research presents tensegrity articulated joints with actuation that combine thin pneumatic artificial muscles and energy-restoring elastics, both integrated into the tensile network. It uses a tensegrity spine-inspired topology, further refined through a multi-objective, constraint-based evolutionary algorithm. The method was validated by designing and fabricating two types of joints, which were tested in a quadruped robot and gripper application. More details can be found in the Research Article by Jan Petrš and co-workers (Doi: 10.1002/aisy.202500310).