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

Loading individual oocytes is a critical step for injecting RNA, expressing heterologous proteins, performing electrophysiological measurements, and so on. However, existing methods remain a challenge for automatically loading multiple single oocytes into different locations. Herein, a novel microfluidic chip on a robotic manipulator (chip-on-robot) with feedback control for flexible manipulation of multiple oocytes within a large spatial range is proposed. The manipulator automatically controls the microfluidic chip to reach different locations based on imaging feedback. The microfluidic chip then utilizes the hydrodynamic focusing effect of the main channel to separate oocytes for individual loading or reloading under capacitive sensor feedback. The separation distance reaches approximately 16 times the oocyte diameter. Moreover, capacitive signal feedback on the number of oocytes for flow direction control ensures the separation of all oocytes. For close-loop control of the loading/reloading process, image-based oocyte detection is combined using deep learning to calculate the target position of the oocyte. Finally, an automatic sequence is achieved to load multiple single oocytes into a well chip by using the chip-on-robot. As a demonstration, the oocytes are reloaded into a specified location based on the conditions. The proposed chip-on-robot with feedback control has significant advantages in the micromanipulation of oocytes.

Tactile sensing in the human body is achieved via the skin. This has inspired the fabrication of synthetic skins with pressure sensors for potential applications in robotics, bio-medicine, and human–machine interfaces. Tactile sensors based on magnetic elements are promising as they provide high sensitivity and a wide dynamic range. However, current magnetic tactile sensors mostly detect pressures of solid objects and operate at relatively high forces about 100 mN. Herein, these limitations are addressed by manufacturing soft, stretchable, and hair-like structures that are permanently magnetized to achieve high-resolution, cost-effective, and high-resolution pressure sensing. Combining these hair-like structures with advances in 3D magnetic-field measurements allows us to monitor directional tactile pressures without solid contact. To prove the concept of this technology, a bio-inspired soft device is built with a hairy structure that senses and reports environmental mechanical stresses, similar to that of human skin. Simple self-assembly of the soft magnetic hair structure makes our approach easy to scale for large-area applications.

Although large-scale pretrained convolutinal neural networks (CNN) models have shown impressive transfer learning capabilities, they come with drawbacks such as high energy consumption and computational cost due to their potential redundant parameters. This study presents an innovative weight-level pruning technique that mitigates the challenges of overparameterization, and subsequently minimizes the electricity usage of such large deep learning models. The method focuses on removing redundant parameters while upholding model accuracy. This methodology is applied to classify Eimeria species parasites from fowls and rabbits. By leveraging a set of 27 pretrained CNN models with a number of parameters between 3.0M and 118.5M, the framework has identified a 4.8M-parameter model with the highest accuracy for both animals. The model is then subjected to a systematic pruning process, resulting in an 8% reduction in parameters and a 421M reduction in floating point operations while maintaining the same classification accuracy for both fowls and rabbits. Furthermore, unlike the existing literature where two separate models are created for rabbits and fowls, this article presents a combined model with 17 classes. This approach has resulted in a CNN model with nearly 50% reduced parameter size while retaining the same accuracy of over 90%.

Two of the main challenges in origami antenna designs are creating a reliable hinge and achieving precise actuation for optimal electromagnetic (EM) performance. Herein, a waterbomb origami ring antenna is introduced, integrating the waterbomb origami principle, 3D-printed liquid metal (LM) hinges, and robotic shape morphing. The approach, combining 3D printing, robotic actuation, and innovative antenna design, enables various origami folding patterns, enhancing both portability and EM performance. This antenna's functionality has been successfully demonstrated, displaying its communication capabilities with another antenna and its ability to navigate narrow spaces on a remote-controlled wheel robot. The 3D-printed LM hinge exhibits low DC resistance (200 ± 1.6 mΩ) at both flat and folded state, and, with robotic control, the antenna achieves less than 1° folding angle accuracy and a 66% folding area ratio. The antenna operates in two modes at 2.08 and 2.4 GHz, ideal for fixed mobile use and radiolocation. Through extensive simulations and experiments, the antenna is evaluated in both flat and folded states, focusing on resonant frequency, gain patterns, and hinge connectivity. The findings confirm that the waterbomb origami ring antenna consistently maintains EM performance during folding and unfolding, with stable resonant frequencies and gain patterns, proving the antenna's reliability and adaptability for use in portable and mobile devices.

Due to the absence of more efficient diagnostic tools, the spread of mpox continues to be unchecked. Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast-MpoxNet is proposed. Fast-MpoxNet, with only 0.27 m parameters, can process input images at 68 frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast-MpoxNet incorporates an attention-based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast-MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early-stage mpox is 93.65%. Most importantly, an application system named Mpox-AISM V2 is developed, suitable for both personal computers and smartphones. Mpox-AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real-time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real-time diagnostic tools in the healthcare field.

Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing capabilities still presents challenges. To overcome this limitation, a fluidic soft gripper is introduced based on the buckling of soft, thin hemispherical shells. Leveraging a single fluidic pressure input, the soft gripper can grasp slippery and delicate objects while passively providing information on this physical interaction. Guided by analytical, numerical, and experimental tools, the novel grasping principle of this mechanics-based soft gripper is explored. First, the buckling behavior of a free hemisphere is characterized as a function of its geometric parameters. Inspired by the free hemisphere's two-lobe mode shape ideal for grasping purposes, it is demonstrated that the gripper can perform dexterous manipulation and gentle gripping of fragile objects in confined spaces and underwater environments. Last, the soft gripper's embedded capability of detecting contact, grasping, and release conditions during the interaction with an unknown object is proved. This simple buckling-based soft gripper opens new avenues for the design of adaptive gripper morphologies with tactile sensing capabilities for applications ranging from medical and agricultural robotics to space and underwater exploration.

Most in vitro studies regarding new anticancer treatments are performed on 2D cultures, despite this approach imposes several limitations in recapitulating the real tumor behavior and in predicting the effects of therapy on both cancer and healthy tissues. Herein, advanced in vitro models based on scaffolds that support the 3D growth of glioma cells, further allowing the cocultures with healthy brain cells, are presented. These scaffolds, doped with superparamagnetic iron oxide nanoparticles and obtained through 2-photon polymerization, can be remotely manipulated thanks to an external magnet, thus obtaining biomimetic 3D organization recapitulating the brain cancer microenvironment. From a geometric point of view, the structure is functional to both cell culture on individual unit scaffolds and to tailored cocultures fostered by magnetic-driven unit assembly, also allowing for cell migration thanks to passages/fenestrations on adjacent structures. Leveraging magnetic dragging, for which a mathematical model is introduced, multiple cocultures are achieved, highlighting the high versatility and the user-friendly character of the proposed platform that can help overcome the current challenges in 3D cocultures handling, and open the way to the construction of increasingly biomimetic artificial systems.

Small-scale magnetic robots are extensively recognized as promising untethered devices that can be controlled externally for numerous microscale applications. This study is proposed to address the independent control of multiple magnetic millirobots using an array of electromagnetic coils. Herein, each of the fabricated magnetic millirobots is magnetized with a dissimilar magnetization profile. Further, these millirobots are independently controlled using the mentioned magnetization strategy in addition to the supply of controlled current to each electromagnetic coil. To explore the physics of this combined stepwise approach in controlling the millirobots, theoretical and numerical investigations are carried out that further ensure the practical significance for broad applications. For demonstration purposes, three different shear-induced flow manipulation experiments, including the particle manipulation task, fluid color transition task, and micromixing task, are conducted using more than one millirobot with distinct motions. A maximum of three millirobots controlled with different motions are employed in the micromixing task, and further, it is observed to achieve nearly 80% mixing efficiency within 45 s. The presented work with the introduced actuation system and motion control strategies can strengthen the existing methods of small-scale robots for various applications, particularly for tasks that demand multiple millirobots.

Grey wolf optimizer (GWO) is a highly valued heuristic algorithm in many fields. However, for some complex problems, especially high-dimensional and multimodal problems, the basic algorithm has limited computational power and cannot get a satisfactory answer. In order to find a better solution, an improved algorithm based on GWO is proposed herein. Gaussian barebone, random selection and chaotic game mechanisms are introduced into the GWO algorithm to enhance the global search ability. The GWO enhanced by three mechanisms is called CBRGWO. To verify the performance of CBRGWO, using IEEE CEC 2017 as a test function, CBRGWO is compared to five GWO variants, five basic algorithms, six advanced algorithms, and four champion algorithms. CBRGWO is evaluated using the Friedman test and Wilcoxon signed-rank test. Then, the stability of CBRGWO is analyzed. To verify that CBRGWO is still effective in practical application, CBRGWO is applied to five engineering problems and a water quality prediction problem. The experimental findings indicate that CBRGWO maintains excellent optimization ability in practical engineering problems.

The rapid advancement of soft robotic technology emphasizes the growing importance of tactile perception. Soft grippers, equipped with tactile sensing, can gather interactive information crucial for safe human–robot interaction, wearable devices, and dexterous manipulation. However, most soft grippers with tactile sensing abilities have limited modes of tactile perception, restricting their dexterity and safety. In addition, existing tactile systems are often complicated, leading to unstable perception signals. Inspired by various organisms, a novel multimodal tactile-sensing soft robotic finger is proposed. This finger, based on a modified fin ray structure, integrates a distributed fiber optic sensing system as part of its tactile sensory neural system. It replicates human finger capabilities, discerning contact forces as low as 0.01 N with exceptional sensitivity (106.96 mN nm⁻¹). Through training neural networks models, the finger achieves an accuracy exceeding 96% in recognizing roughness, material stiffness, and finger pad position. Assembled into two-finger parallel gripper, it demonstrates precise manipulation capabilities for fragile items like strawberries and potato chips. Moreover, through synergistic interplay of multimodal tactile sensing, this finger can successfully grasp an underwater transparent sphere, mitigating limitations of visual perception. The developed soft finger holds promise in various scenarios including hazardous environment detection and specialized grasping tasks.