Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.

With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand-tuned signals.

Single-step precision programming of decoupled multiresponsive soft millirobots.

Stimuli-responsive soft robots offer new capabilities for the fields of medical and rehabilitation robotics, artificial intelligence, and soft electronics. Precisely programming the shape morphing and decoupling the multiresponsiveness of such robots is crucial to enable them with ample degrees of freedom and multifunctionality, while ensuring high fabrication accuracy. However, current designs featuring coupled multiresponsiveness or intricate assembly processes face limitations in executing complex transformations and suffer from a lack of precision.

Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation.

Bioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual-free, repeatable, self-cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance.

Size-Dependent Locomotion Ability of Surface Microrollers on Physiologically Relevant Microtopographical Surfaces.

Controlled microrobotic navigation inside the body possesses significant potential for various biomedical engineering applications. Successful application requires considering imaging, control, and biocompatibility. Interaction with biological environments is also a crucial factor in ensuring safe application, but can also pose counterintuitive hydrodynamic barriers, limiting the use of microrobots.

Electrodeposited Superhydrophilic-Superhydrophobic Composites for Untethered Multi-Stimuli-Responsive Soft Millirobots.

To navigate in complex and unstructured real-world environments, soft miniature robots need to possess multiple functions, including autonomous environmental sensing, self-adaptation, and multimodal locomotion. However, to achieve multifunctionality, artificial soft robots should respond to multiple stimuli, which can be achieved by multimaterial integration using facile and flexible fabrication methods. Here, a multimaterial integration strategy for fabricating soft millirobots that uses electrodeposition to integrate two inherently non-adherable materials, superhydrophilic hydrogels and superhydrophobic elastomers, together via gel roots is proposed.

Programmable aniso-electrodeposited modular hydrogel microrobots.

Systems with programmable and complex shape morphing are highly desired in many fields wherein sensing, actuation, and manipulation must be performed. Living organisms use nonuniform distributions of their body structural composition to achieve diverse shape morphing, motion, and functionality. However, for the microrobot fabrication, these designs often involve complicated robotic architectures requiring time-consuming and arduous fabrication processes.

Task space adaptation via the learning of gait controllers of magnetic soft millirobots.

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot's motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions.

Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots.

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time, which would have deteriorating effects on the robot motion and task performance.

Evaluation of the concordance between the stage of the disease and Ki-67 proliferation index in gastroenteropancreatic neuroendocrine tumors.

Aim: This study aimed to determine the degree of concordance between TNM staging used in the determination of the prognosis of gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients and the Ki-67 proliferation index value used in the grading of these tumors and investigate the most reliable prognostic parameter among them.

Materials And Methods: The medical files of the patients with GEP-NET who were diagnosed or followed up in Erciyes University Faculty of Medicine were retrospectively examined and demographic characteristics, survival times, grade of these tumors, histopathologically detected Ki-67 values, and histopathological characteristics were recorded and evaluated statistically.

Results: The mean age (53.

Optimization of struvite fertilizer formation from baker's yeast wastewater: growth and nutrition of maize and tomato plants.

Struvite precipitate obtained from yeast industry anaerobic effluent with high ammonium nitrogen (NH4-N) was investigated for fertilizer effect on plant growth and nutrition according to applications of N, nitrogen/phosphorus/potassium (NPK), and control. Optimum struvite formation conditions were determined via Box-Behnken design. Optimum condition was obtained at pH 9.