CFD investigation of a complete floating offshore wind turbine

This chapter presents numerical computations for floating offshore wind turbines for a machine of 10-MW rated power. The rotors were computed using the Helicopter Multi-Block flow solver of the University of Glasgow that solves the Navier-Stokes equations in integral form using the arbitrary Lagrangian-Eulerian formulation for time-dependent domains with moving boundaries. Hydrodynamic loads on the support platform were computed using the Smoothed Particle Hydrodynamics method. This method is mesh-free, and represents the fluid by a set of discrete particles. The motion of the floating offshore wind turbine is computed using a Multi-Body Dynamic Model of rigid bodies and frictionless joints. Mooring cables are modelled as a set of springs and dampers. All solvers were validated separately before coupling, and the loosely coupled algorithm used is described in detail alongside the obtained results

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Robotic surgery has been proven to offer clear advantages during surgical procedures, however, one of the major limitations is obtaining haptic feedback. Since it is often challenging to devise a hardware solution with accurate force feedback, we propose the use of "visual cues" to infer forces from tissue deformation. Endoscopic video is a passive sensor that is freely available, in the sense that any minimally-invasive procedure already utilizes it. To this end, we employ deep learning to infer forces from video as an attractive low-cost and accurate alternative to typically complex and expensive hardware solutions. First, we demonstrate our approach in a phantom setting using the da Vinci Surgical System affixed with an OptoForce sensor. Second, we then validate our method on an ex vivo liver organ. Our method results in a mean absolute error of 0.814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.Comment: MICCAI 2018 workshop, CARE(Computer Assisted and Robotic Endoscopy

The relationship between resilience and quality of life in family caregivers of patients with mental disorders

Introduction: In the past, patients with mental disorders were often isolated, but these patients now-a-days enter the society, as therapeutic interventions have advanced. Family members play an important role in the life of many adults with mental disorders and are under considerable amounts of stress that may affect caregiver’s physical health, quality of life and resilience. Aim: The present study aimed to determine the relationship between the resilience and quality of life in family caregivers of patients with mental disorders. Materials and Methods: The present cross-sectional, correlational, descriptive study was conducted on 238 family caregivers of patients with mental disorders. The Short Form Health Survey (SF-36) was used to measure the quality of life and the Connor and Davidson Resilience Scale was used to measure resilience in the participants. The SF-36 consists of two general dimensions and eight domains of health and the resilience scale consists of 25 items. The data obtained through the questionnaires were analysed in SPSS version 16.0 using Pearson’s correlation test. Results: The majority of the family caregivers were the patients’ mothers. The results showed a significant direct relationship between resilience and quality of life (p<0.001, r=0.40). Conclusion: Resilience is a personal resource that affects quality of life directly. Resilience can enhance quality of life. The design and implementation of programs to enhance resilience and improve quality of life in family caregivers in line with the emerging needs of this group are therefore necessary. © 2018, Journal of Clinical and Diagnostic Researc

Highly Living Stars via Core-First Photo-RAFT Polymerization: Exploitation for Ultra-High Molecular Weight Star Synthesis

Star polymers are highly functional materials that display unique properties in comparison to linear polymers, making them valuable in a wide range of applications. Currently, ultra-high molecular weight (UHMW) star polymers synthesized using controlled radical polymerization are prone to termination reactions that have undesirable effects, such as star-star coupling. Herein, we report the synthesis of the largest star polymers to date using controlled radical techniques via xanthate-mediated photo-reversible addition-fragmentation chain transfer (RAFT) polymerization using a core-first approach. Polymerization from xanthate-functionalized cores was highly living, enabling the synthesis of well-defined star polymers with molecular weights in excess of 20 MDa