Encounter rate by turbulent shear of particles similar in diameter to the Kolmogorov scale

To clarify the rate at which particles similar in size to the smallest eddies in a turbulent fluid encounter one another via turbulent shear, 3-D video motion analysis was used to make direct measurements of relative velocities between closely spaced, near-neutrally buoyant, 700-μm mean diameter, polystyrene latex spheres suspended in an oscillating-grid turbulence tank. Smallest eddy size, termed the Kolmogorov scale, λ, was estimated as (ν3/ε)0.25 where ν is fluid viscosity and ε is the dissipation rate of turbulent kinetic energy. For runs made in water, the effective particle diameter examined was ≈ 3–6 times larger than λ. To measure relative velocities for particles just smaller than the Kolmogorov scale, the viscosity of the suspending fluid was increased ≈ 25 times by the addition of Methocel, a commercially available, methyl cellulose synthetic gum used for fluid thickening. For runs made in Methocel, effective sphere diameter was ≈ 0.2–0.5 times the Kolmogorov scale. Turbulent kinetic energy dissipation rate was estimated by traversing the measuring volume of a laser-Doppler velocimeter fiberoptic probe through the fluid at speeds high relative to the fluctuating fluid velocities in the tank. Resulting time series were used in analogy with instantaneous spatial series to calculate root-mean-square fluctuating velocities and integral length scales of turbulence, which in turn served as input for calculation of ε. By examining the relationship between Reynolds number based on relative velocity between particles and particle separation distance relative to λ, two competing hypotheses were tested. The first, that turbulent eddying motions control relative velocity between closely spaced particles, was accepted for particles both slightly larger and slightly smaller than the Kolmogorov scale (0.05 \u3c p \u3c 0.10). The second, that viscous forces control relative velocity between particles, was strongly rejected in both cases (p = 0.004). The finding contradicts earlier assumptions and assertions that viscosity dominates small-scale particle interactions for sizes near the Kolmogorov scale, and it indicates that relative velocities between particles are greater than previously thought. Relative to biological mechanisms of particle encounter, turbulence therefore plays a role greater than is presently assumed in effecting encounter among particles and also between particles and organisms

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

Role of the iodide–methylammonium interaction in the ferroelectricity of CH3NH3PbI3

Excellent conversion efficiencies of over 20 % and facile cell production have placed hybrid perovskites at the forefront of novel solar cell materials, with CH3NH3PbI3 being an archetypal compound. The question why CH3NH3PbI3 has such extraordinary characteristics, particularly a very efficient power conversion from absorbed light to electrical power, is hotly debated, with ferroelectricity being a promising candidate. This does, however, require the crystal structure to be non‐centrosymmetric and we herein present crystallographic evidence as to how the symmetry breaking occurs on a crystallographic and, therefore, long‐range level. Although the molecular cation CH3NH3+ is intrinsically polar, it is heavily disordered and this cannot be the sole reason for the ferroelectricity. We show that it, nonetheless, plays an important role, as it distorts the neighboring iodide positions from their centrosymmetric positions

Ultrasonic monitoring of friction contacts during shear vibration cycles

Complex high-value jointed structures such as aero-engines are carefully designed and optimized to prevent failure and maximise their life. In the design process, physically-based numerical models are employed to predict the nonlinear dynamic response of the structure. However, the reliability of these models is limited due to the lack of accurate validation data from metallic contact interfaces subjected to high-frequency vibration cycles. In this study, ultrasonic shear waves are used to characterise metallic contact interfaces during vibration cycles, hence providing new validation data for an understanding of the state of the friction contact. Supported by numerical simulations of wave propagation within the material, a novel experimental method is developed to simultaneously acquire ultrasonic measurements and friction hysteresis loops within the same test on a high-frequency friction rig. Large variability in the ultrasound reflection/transmission is observed within each hysteresis loop and is associated with stick/slip transitions. The measurement results reveal that the ultrasound technique can be used to detect stick and slip states in contact interfaces subjected to high-frequency shear vibration. This is the first observation of this type and paves the way towards real-time monitoring of vibrating contact interfaces in jointed structures, leading to a new physical understanding of the contact states and new validation data needed for improved nonlinear dynamic analyses

The evolution of interdisciplinarity in physics research

Science, being a social enterprise, is subject to fragmentation into groups that focus on specialized areas or topics. Often new advances occur through cross-fertilization of ideas between sub-fields that otherwise have little overlap as they study dissimilar phenomena using different techniques. Thus to explore the nature and dynamics of scientific progress one needs to consider the large-scale organization and interactions between different subject areas. Here, we study the relationships between the sub-fields of Physics using the Physics and Astronomy Classification Scheme (PACS) codes employed for self-categorization of articles published over the past 25 years (1985-2009). We observe a clear trend towards increasing interactions between the different sub-fields. The network of sub-fields also exhibits core-periphery organization, the nucleus being dominated by Condensed Matter and General Physics. However, over time Interdisciplinary Physics is steadily increasing its share in the network core, reflecting a shift in the overall trend of Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway

Navigability is a Robust Property

The Small World phenomenon has inspired researchers across a number of fields. A breakthrough in its understanding was made by Kleinberg who introduced Rank Based Augmentation (RBA): add to each vertex independently an arc to a random destination selected from a carefully crafted probability distribution. Kleinberg proved that RBA makes many networks navigable, i.e., it allows greedy routing to successfully deliver messages between any two vertices in a polylogarithmic number of steps. We prove that navigability is an inherent property of many random networks, arising without coordination, or even independence assumptions

A reaction-diffusion model for the growth of avascular tumor

A nutrient-limited model for avascular cancer growth including cell proliferation, motility and death is presented. The model qualitatively reproduces commonly observed morphologies for primary tumors, and the simulated patterns are characterized by its gyration radius, total number of cancer cells, and number of cells on tumor periphery. These very distinct morphological patterns follow Gompertz growth curves, but exhibit different scaling laws for their surfaces. Also, the simulated tumors incorporate a spatial structure composed of a central necrotic core, an inner rim of quiescent cells and a narrow outer shell of proliferating cells in agreement with biological data. Finally, our results indicate that the competition for nutrients among normal and cancer cells may be a determinant factor in generating papillary tumor morphology.Comment: 9 pages, 6 figures, to appear in PR

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page