Computational Modeling and Simulation of Stenosis of Aqueduct of Sylvius Due to Brain Tumor

Background: Stenosis of cerebral aqueduct (CA) is featured in many studies related to elevated intracranial cerebral pressures (ICP). It also presents a challenging situation to clinicians. Compressive forces play a lead role in pathological situations like tumor presence and hence can cause obstruction to the flow of cerebrospinal fluid (CSF). Due to this barrier, excessive retention of CSF in ventricles can occur. This in turn could contribute to increased pressure gradients inside the cranium. In literature, most of the numerical models are restricted to modeling the CSF flow by considering ventricle walls as rigid material unlike its behavior a deformable character. This paper, therefore, addresses the same from a holistic perspective by taking into consideration the dynamics of the flexible character of the ventricular wall. This adds to the novelty of this work by reconstructing an anatomically realistic ventricular wall behavior. To do this, the authors aim to develop a computational model of stenosis of CA due to brain tumor by invoking a fluid-structure interaction (FSI) method. The proposed 3D FSI model is simulated under two cases. First, simulation of pre-stenosis case with no interaction of tumor forces and secondly, a stenosis condition together-with dynamic interaction of tumor forces. Results: Comparing the forces with and without tumor reveals a marked obstruction of CSF outflow post third ventricle and the cerebral aqueduct. Not only this but a drastic rise of CSF velocity from 21.2 mm/s in pre-stenosis case to 54.1 mm/s stenosis case is also observed along with a net deformation increase of 0.144 mm on walls of ventricle. Conclusions: This is a significant contribution to brain simulation studies for pressure calculations, wherein the presence of tumors is a major concern.

Requirements for Computational Approaches to Analyzing Resilience in Human-Machine Teams

Human-machine teams (HMTs) in complex work domains need to be able to adapt to variable and uncertain work demands. Computational modeling and simulation can provide novel approaches to the evaluation of HMTs performing complex joint activities, affording large-scale, quantitative analysis of team characteristics (such as system architecture and governance protocols) and their effects on resilience. Drawing from literature in resilience engineering, human-automation interaction, and cognitive systems engineering, this paper provides a theoretical exploration of the use of computational modeling and simulation to analyze resilience in HMTs. Findings from literature are summarized in a set of requirements that highlight key aspects of resilience in HMTs that need to be accounted for in future modeling and evaluation efforts. These requirements include a need to model HMTs as joint cognitive systems, the need to account for the interdependent nature of activity, the temporal dynamics of work, and the need to support formative exploration and inquiry. We provide a brief overview of existing modeling and simulation approaches to evaluating HMTs and discuss further steps for operationalizing the identified requirements.

Computational Generation of Virtual Concrete Mesostructures

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behavior of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modeling and simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.

Computational Modeling and Simulation to Increase Laser Shooting Accuracy of Autonomous LEO Trackers

In this paper, we introduce a computational procedure that enables autonomous LEO laser trackers endowed with INSs to increase the current accuracy when shooting at middle distant medium-size LEO debris targets. The code is designed for the trackers to throw the targets into the atmosphere by means of ablations. In case that the targets are eclipsed to the trackers by the Earth, the motions of the trackers and targets are modeled by equations that contain post-Newtonian terms accounting for the curvature of space. Otherwise, when the approaching targets become visible for the trackers, we additionally use more accurate equations, which allow to account for the local bending of the laser beams aimed at the targets. We observe that under certain circumstances the correct shooting configurations that allow to safely and efficiently shoot down the targets, differ from the current estimations by distances that may be larger than the size of many targets. In short, this procedure enables to estimate the optimal shooting instants for any middle distant medium-size LEO debris target.

Computational Modeling and Simulation to increase Laser Shooting Accuracy of Autonomous LEO Trackers

In this paper, we introduce a computational procedure that enables autonomous LEO laser trackers endowed with INSs to increase the current accuracy when shooting at middle distant medium-size LEO debris targets. The code is designed for the trackers to throw the targets into the Atmosphere by means of ablations. In case that the targets are eclipsed to the trackers by the Earth, the motions of the trackers and targets are modeled by equations that contain post-Newtonian terms accounting for the curvature of space. Otherwise, when the approaching targets become visible for the trackers, we additionally use more accurate equations, which allow to account for the local bending of the laser beams aimed at the targets. We observe that under certain circumstances the correct shooting configurations that allow to safely and efficiently shoot down the targets, differ from the current estimations by distances that may be larger than the size of many targets. In short, this procedure enables to estimate the optimal shooting instants for any middle distant medium-size LEO debris target.

A Short Note on the General Aspects of Drug Designing

Emerging and re-emerging diseases are expanding round the globe which drew the mass public health in dreadful condition. Microbial resistance to drugs is a complicated issue for the failure of treatment of a variety of diseases. In this circumstance, designing of appropriate drug(s) is essential which usually involves the computational modeling and simulation followed by cell culture/ animal model experiments, ending up to clinical trials. Current review briefly focused on the general aspects of drug manufacturing; and a short discussion on the fine tune basis of drug designing grounded on the previously published literature.