Let K be the field of fractions of a local Henselian discrete valuation ring O K of characteristic zero with perfect residue field k. Assuming potential semi-stable reduction, we show that an unramified Galois action on the second -adic cohomology group of a K3 surface over K implies that the surface has good reduction after a finite and unramified extension. We give examples where this unramified extension is really needed. Moreover, we give applications to good reduction after tame extensions and Kuga-Satake Abelian varieties. On our way, we settle existence and termination of certain flops in mixed characteristic, and study group actions and their quotients on models of varieties.

We prove a Neron--Ogg--Shafarevich type criterion for good reduction of K3 surfaces, which states that a K3 surface over a complete discrete valuation field has potential good reduction if its $l$-adic cohomology group is unramified. We also prove a $p$-adic version of the criterion. (These are analogues of the criteria for good reduction of abelian varieties.) The model of the surface will be in general not a scheme but an algebraic space. As a corollary of the criterion we obtain the surjectivity of the period map of K3 surfaces in positive characteristic.Comment: 31 Pages, Accepted version (plus minor modifications on Remark 1.2(2), Proposition 2.2(4), Section 5.3, Remark 6.2

To minimize the interference that skin-contact strain sensors cause natural skin deformation, physical conformability to the epidermal structure is critical. Here, we developed an ultrathin strain sensor made from poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) inkjet-printed on a polystyrene–polybutadiene–polystyrene (SBS) nanosheet. The sensor, whose total thickness and gauge factor were ∼1 µm and 0.73 ± 0.10, respectively, deeply conformed to the epidermal structure and successfully detected the small skin strain (∼2%) while interfering minimally with the natural deformation of the skin. Such an epidermal strain sensor will open a new avenue for precisely detecting the motion of human skin and artificial soft-robotic skin.

Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

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