Control Strategy of Vibrational Capsubot in Viscoelastic Environment

Active capsule endoscopy is becoming a research hotspot in recent years. We design an active capsule robot (capsubot) with the vibrational mode. The internal force-static friction control strategy which is used in the capsubot is effective in rigid environment but not in viscoelastic environment. A particular viscoelastic material whose parameters are confirmed is set to the viscoelastic environment. We suppose that it is a periodic damped oscillation system when the capsubot make a free vibration in the environment. We propose a new control strategy whose principle is similar to a swing in the environment. The simulation results show that the new strategy is effective.

The influence of probe lift-up height on CNT electrical properties measurement under EFM DC mode

Nowadays,one of the bottlenecks which hinder the development and application of carbon nanotube(CNT)nano device is that no pure semiconducting CNT(s-CNT)or metallic CNT(m-CNT)can be obtained,and for solving this problem scientists proposed some methods on preparation or separation,but all the results still should be detected and feedback to the process for further improving the preparation and separation methods.Thus,it is very important to measure and distinguish the electrical properties of CNT.For that,scientists proposed a method to measure CNT electrical properties based on DC electrostatic force microscope(EFM)mode,which distinguishes m-CNT from s-CNT according to different scan line shape to CNT with different electrical properties.But,we discovered that the probe lift-up height will seriously affect the shape of the scan line,which makes this method not reliable in distinguishing m-CNT from s-CNT.In this paper,the authors deeply researched the influence of probe lift-up height and also gave corresponding theoretical analysis and explanation,which will greatly improve the method of detecting CNT electrical properties by EFM.

Experimental study on the effects of shear stress on viscoelastic properties of the intestines

Shear deformation induced by shear stress is one of the major forms of interaction between the locomotion mechanism and the intestines. Relatively few experimental studies using locomotion mechanisms have been performed to investigate the viscoelastic property of intestines. There is a lack of reliable data regarding the relative movement between various locomotion mechanisms and the small intestine, and how that movement affects the measurement of shear displacement. In this work, a novel platform was constructed with two elaborately designed clamps that could securely fix both sides of an intestine sample using negative gas adsorption. The platform was also integrated with noncontact measuring equipment to record the thickness of the intestine sample. Subsequently, preservation measure was applied to porcine intestine, and multiple intestine samples were prepared and tested under strains of 20%, 50%, 80% and 100%. A five-element viscoelastic model that was fitted to multiple sets of data could accurately predict the biomechanical property of the intestines. Finally, a new criterion, the loss factor, was calculated with the parameters of the five-element model to represent the dynamic behavior of soft tissue, and it was used to verify the reliability and effectiveness of the experimental setup and results.

Detecting the electrical conductivity of single walled carbon nano-tubes by a DFM detection system

Rare SWCNT materials contain both metallic SWCNT （m-SWCNT） and semi-conducting SWCNT（s-SWCNT）. Since m- SWCNT and s-SWCNT have very different applications, it is necessary to differentiate them so as to further separate them for more efficient CNT utilization. To achieve this goal, the authors established a dielectric force microscope （DFM） detection system to differentiate s-SWCNT from m-SWCNT, based on different 2c~ force decided by SWCNT＇s conductivity under AC electric field. The experimental results showed that s-SWCNT can be clearly differentiated from m-SWCNT. The statistics analysis shows that the detected number proportion of s-SWCNT to m-SWCNT matches the well-known proportion 2：1 in the normally prepared CNT materials. The above results strongly verified the effectiveness of the detection system.

Foldable Units and Wing Expansion of the Oakleaf Butterfly During Eclosion

Eclosion is a rapid process of morphological changes in insects,especially for the wings of butterflies.The orange oakleaf butterfly(Kallima inachus)transits from pupae to adults with a 9.3 fold instant increase in the surface area of their wings.To explore the mechanism for the rapid morphological changes in butterfly wings,we analyzed changes in microstructures in the wings of K.inachus.We found that there were lots of micron-sized foldable units in the wings at the pupal stage.The foldable units could provide as much as 31.35 times of increase in wing surface area.During eclosion,foldable units were flattened sequentially and resulted in a rapid increase in wing surface areas.The unfolding process was regulated by the structures and layouts of wing veins.Based on our observation,foldable units play important roles in both deformation and stretching of wings.The foldable units of microstructures may provide mimics for simulating entities of large-deformational bionic structures with practical application.

DeceFL:a principled fully decentralized federated learning framework

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently fragmented.Such a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy concerns.Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client.Here we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning framework.It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly convex.Finally,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.