This paper presents a compliant end-effector that cuts soft tissues and senses the cutting forces. The end-effector is designed to have an upper threshold on cutting forces to facilitate safe handling of tissue during automated cutting. This is demonstrated with nonlinear finite element analysis and experimental results obtained by cutting inhomogeneous phantom tissue. The cutting forces are estimated using a vision-based technique that uses amplified elastic deformation of the compliant end-effector. We also demonstrate an immersive tele-operated tissue-cutting system together with a haptic device that gives real-time force feedback to the user.
How do we assess the capability of a compliant mechanism of given topology and shape? The kinetoelastostatic maps proposed in this paper help answer this question. These maps are drawn in 2D using two non-dimensional quantities, one capturing the nonlinear static response and the other the geometry, material, and applied forces. Geometrically nonlinear finite element analysis is used to create the maps for compliant mechanisms consisting of slender beams. In addition to the topology and shape, the overall proportions and the proportions of the cross-sections of the beam segments are kept fixed for a map. The finite region of the map is parameterized using a non-dimensional quantity defined as the slenderness ratio. The shape and size of the map and the parameterized curves inside it indicate the complete kinetoelastostatic capability of the corresponding compliant mechanism of given topology, shape, and fixed proportions.
Static responses considered in this paper include input/output displacement, geometric amplification, mechanical advantage, maximum stress, etc. The maps can be used to compare mechanisms, to choose a suitable mechanism for an application, or re-design as may be needed. The usefulness of the non-dimensional maps is presented with multiple applications of different variety. Non-dimensional portrayal of snap-through mechanisms is one such example. The effect of the shape of the cross-section of the beam segments and the role of different segments in the mechanism as well as extension to 3D compliant mechanisms, the cases of multiple inputs and outputs, and moment loads are also explained. The effects of disproportionate changes on the maps are also analyzed.
Patients underwent intracytoplasmic sperm injection and had at least one GV oocyte after denudation were included. After denudation, GV oocytes were cultured in G-IVFÒ media and placed into a time-lapse incubator. Images were taken every 10 mins for 144 hours. The GVBD and PBE time were counted. Patient's age, protocol, base and hCG day luteinizing hormone (LH), base follicle stimulation hormone (FSH), base and hCG day estradiol (E2), immaturation rate, and big follicle acquisition rate (BFA, BFA¼[N of retrieval oocyte]/[N of folli-clesR12mm]) were recorded for univariable clustered Cox regression [1,2]. Variables (p <0.3) were chosen for multivariable analysis. Hazard ratio (HR) with 95% confidence interval (95%CI) were reported.RESULTS: There were 36 patients (79 GV oocytes) recruited. 12 GV oocytes did not mature. The overall time-course was 23.2h (95%CI 21.3-24.4h). The baseline and analysis results are shown in Table I. The BFA<1 means GV oocytes were from big follicles. GV oocytes in group BFA<1 showed shortened time-course in both univariable (HR 2.43, 95%CI 1.49-4.35, p< .001) and multivariable analysis (HR 2.38, 95%CI 1.32-4.35, p¼0.004). The adjusted chance of maturation in group base E2 concentration >50 was revealed to be two times greater (HR 2.00 95%CI 1.06-3.81, p¼0.034).CONCLUSIONS: We first demonstrate a precise time-course of GVBD to PBE in stimulated cycles, which can contribute significantly to catching the fertilization window in r-IVM as well as traditional IVM. We found that GV oocytes from big follicles or patient with higher base E2 have higher chance of maturation. These findings give clues for oocyte and follicle development, but further studies are needed to confirm.References: [1] Moore, D. F. (2016). Applied survival analysis using R, Springer.[2] Glidden, D. V. and E. Vittinghoff "Modelling clustered survival data from multicentre clinical trials." Statistics in medicine 23(3): 369-388. (2004).''
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