arXiv subjects

Suvranu De

Publications and source records attributed to Suvranu De.

25 records · Page 2Linked to original sources

arXiv2020

Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging

Acquisition of fine motor skills is a time-consuming process as it requires frequent repetitions. Transcranial electrical stimulation is a promising means of enhancing simple motor skill development via neuromodulatory mechanisms. Here, we report that non-invasive neurostimulation facilitates the learning of complex fine bimanual motor skills associated with a surgical task. During the training of 17 medical students on the Fundamentals of Laparoscopic Surgery (FLS) pattern cutting task over a period of 12 days, we observed that transcranial direct current stimulation (tDCS) decreased the error level and the variability in performance, compared to the Sham group. By concurrently monitoring the cortical activations of the subjects via functional near-infrared spectroscopy (fNIRS), our study showed that the cortical activation significantly stimulated by tDCS. The lowered performance error and the increased brain activation were retained after one-month post-training. This work supports the use of tDCS to enhance performance accuracy in fine bimanual motor tasks.

physics.med-ph↗

arXiv2020

Multiscale modeling meets machine learning: What can we learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

physics.bio-ph↗

arXiv2019

Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. We show how machine learning and multiscale modeling can complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

q-bio.QM↗

arXiv2014

A first-principles investigation of the equation of states and molecular "weak spots" of $β$-cyclotetramethylene tetranitramine (HMX)

We investigate the equation of states of the $β$-polymorph of cyclotetramethylene tetranitramine (HMX) energetic molecular crystal using DFT-D2, a first-principles calculation based on density functional theory (DFT) with van der Waals (vdW) corrections. The atomic structures and equation of states under hydrostatic compressions are studied for pressures up to 100 GPa. We found that the N-N bonds along the minor axis of the ring are more sensitive to the variation of pressure, which indicates that they are potential "weak spots" in atomic level within a single molecule of $β$-HMX. Our study suggested that the van der Waals interactions are critically important in modeling this molecular crystal.

cond-mat.mtrl-sci↗

arXiv2012

Elastic Properties of Hybrid Graphene/Boron Nitride Monolayer

Recently hybridized monolayers consisting of hexagonal boron nitride (h-BN) phases inside graphene layer have been synthesized and shown to be an effective way of opening band gap in graphene monolayers [1]. In this letter, we report an ab initio density functional theory (DFT)- based study of h-BN domain size effect on the elastic properties of graphene/boron nitride hybrid monolayers (h-BNC). We found both inplane stiffness and longitudinal sound velocity of h-BNC linearly decrease with h-BN concentration.

cond-mat.mtrl-sci↗

arXiv2012

Tunable Band Gaps of Mono-layer Hexagonal BNC Heterostructures

Bandgap engineering by substituting C with B and N atoms in graphene has been shown to be a promising way to improve semiconducting properties of graphene. Such hybridized monolayers consisting of hexagonal BN phases in graphene (h-BNC) have been recently synthesized and char- acterized. In this paper, we present an ab initio density functional theory (DFT)-based study of h-BN domain size effect on band gap of mono-layer h-BNC heterostructures. The atomic structures, electronic band structures, density of states and electron localization functions of five h-BNC config- urations are examined as h-BN concentration ranged from 0 to 100%. We report that the band gap energy of h-BNC can be continuously and quadratically tuned as a function of h-BN concentration.

cond-mat.mtrl-sci↗

arXiv2011

Mechanics properties of Mono-layer Hexagonal Boron Nitride: Ab initio study

We introduced a method to obtain the continuum description of the elastic properties of mono- layer h-BN through ab initio density functional theory. This thermodynamically rigorous contin- uum description of the elastic response is formulated by expanding the elastic strain energy density in a Taylor series in strain truncated after the fifth-order term. we obtained a total of fourteen nonzero independent elastic constants for the up to tenth-order tensor. We predicted the pressure dependent second-order elastic moduli. This continuum formulation is suitable for incorporation into the finite element method.

cond-mat.mtrl-sci↗