Design-thinking, making, and innovating: Fresh tools for the physician\u27s toolbox

Medical school education should foster creativity by enabling students to become \u27makers\u27 who prototype and design. Healthcare professionals and students experience pain points on a daily basis, but are not given the tools, training, or opportunity to help solve them in new, potentially better ways. The student physician of the future will learn these skills through collaborative workshops and having dedicated \u27innovation time.\u27 This pre-clinical curriculum would incorporate skills centered on (1) Digital Technology and Small Electronics (DTSE), (2) Textiles and Medical Materials (TMM), and (3) Rapid Prototyping Technologies (RPT). Complemented by an on-campus makerspace, students will be able to prototype and iterate on their ideas in a fun and accessible space. Designing and making among and between patients and healthcare professionals would change the current dynamic of medical education, empowering students to solve problems in healthcare even at an early stage in their career. By doing so, they will gain empathy, problem-solving abilities, and communication skills that will extend into clinical practice. Our proposed curriculum will equip medical students with the skills, passion, and curiosity to impact the future of healthcare

Interpretation of scanning tunneling quasiparticle interference and impurity states in cuprates

We apply a recently developed method combining first principles based Wannier functions with solutions to the Bogoliubov-de Gennes equations to the problem of interpreting STM data in cuprate superconductors. We show that the observed images of Zn on the surface of Bi$_2$Sr$_2$CaCu$_2$O$_8$ can only be understood by accounting for the tails of the Cu Wannier functions, which include significant weight on apical O sites in neighboring unit cells. This calculation thus puts earlier crude "filter" theories on a microscopic foundation and solves a long standing puzzle. We then study quasiparticle interference phenomena induced by out-of-plane weak potential scatterers, and show how patterns long observed in cuprates can be understood in terms of the interference of Wannier functions above the surface. Our results show excellent agreement with experiment and enable a better understanding of novel phenomena in the cuprates via STM imaging.Comment: 5 pages, 5 figures, published version (Supplemental Material: 5 pages, 11 figures) for associated video file, see http://itp.uni-frankfurt.de/~kreisel/QPI_BSCCO_BdG_p_W.mp

Plasmonic backscattering enhanced inverted photovoltaics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98669/1/ApplPhysLett_99_113306.pd

Modeling Of Lower Extremity For Joint Torques Determination By Performing A Lifting Task

Physical lifting tasks commonly involve two types of body postures,namely, squat lifting and stoop lifting. Studies shows improper bodyposture during lifting task has detrimental effect to human lower-backregion over extended period of time. This is because generally, stoop-liftingposture exerts relatively higher moments and compression forces on humanback than squat lifting posture. However, this claim was never thoroughlyexamined and validated from mathematical model approach. This paperproposes a mathematical model to represent the lower extremity of humanbody during lifting tasks, based on a two-link kinematic open chain in twodimensional spaces. Thus, all moment of torque and their effect to everypart of lower extremity of human body can be thoroughly analyzed

Coercive Field and Magnetization Deficit in Ga(1-x)Mn(x)As Epilayers

We have studied the field dependence of the magnetization in epilayers of the diluted magnetic semiconductor Ga(1-x)Mn(x)As for 0.0135 < x < 0.083. Measurements of the low temperature magnetization in fields up to 3 T show a significant deficit in the total moment below that expected for full saturation of all the Mn spins. These results suggest that the spin state of the non-ferromagnetic Mn spins is energetically well separated from the ferromagnetism of the bulk of the spins. We have also studied the coercive field (Hc) as a function of temperature and Mn concentration, finding that Hc decreases with increasing Mn concentration as predicted theoretically.Comment: 15 total pages -- 5 text, 1 table, 4 figues. Accepted for publication in MMM 2002 conference proceedings (APL

Rabi oscillations of a qubit coupled to a two-level system

The problem of Rabi oscillations in a qubit coupled to a fluctuator and in contact with a heath bath is considered. A scheme is developed for taking into account both phase and energy relaxation in a phenomenological way, while taking full account of the quantum dynamics of the four-level system subject to a driving AC field. Significant suppression of the Rabi oscillations is found when the qubit and fluctuator are close to resonance. The effect of the fluctuator state on the read-out signal is discussed. This effect is shown to modify the observed signal significantly. This may be relevant to recent experiments by Simmonds et al. [Phys. Rev. Lett. 93, 077003 (2004)].Comment: 4 pages, 4 figure

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization