Hemodynamic Differences Between Basilar Artery Fenestration and Normal Vertebrobasilar Artery: A Pilot Study

Background: Basilar artery fenestration has been proposed as a contributor to ischemic stroke, as unique flow patterns induced by fenestration may be related to thrombus formation or insufficiency. This study aimed to evaluate the hemodynamics of basilar artery fenestration (BAF) using computational fluid dynamics (CFD).Methods: Patients with BAF and normal vertebrobasilar system were recruited and separately evaluated using CFD. Specific geometric vascular models were reconstructed based on 3D-rotational angiography (3D-RA). Patients were divided into the BAF group and control group (i.e., patients with the normal vertebrobasilar system). Hemodynamic and geometric variables were calculated and compared between groups using Student's t-test or Wilcoxon rank-sum test.Results: Overall, 24 patients were included, with 12 patients each in the BAF group and the control group. The BAF group had a significantly smaller basilar artery diameter than the control group (3.1 ± 0.51 vs. 3.76 ± 0.4, p = 0.002). Compared to the control group, the BAF group had higher values of maxOSI (median, 0.3 vs. 0.09, p = 0.028), TAWSSG (median, 983.42 vs. 565.39, p = 0.038) in the flow confluence, higher SAR-TAWSSG in bifurcation (median, 70.22 vs. 27.65, p = 0.002) and higher SAR-TAWSSG in basilar artery (median, 48.75 vs. 16.17, p < 0.001) of the vertebrobasilar artery.Conclusions: This pilot study suggested that hemodynamic differences between BAF and normal vertebrobasilar artery across multiple shear flow parameters. The disturbed flow in the BAF may increase the risk of thrombus formation, plaque instability, and subsequent ischemic cerebrovascular events. These should be confirmed by future studies.

Characterization of the Geometric Distribution Via Linear Combinations of Observations and of Records

In a sequence of independent identically distributed geometric random variables, the sum of the first two record values is distributed as a simple linear combination of geometric variables. It is verified that this distributional property characterizes the geometric distribution. A related characterization conjecture is also discussed. Related discussion in the context of weak records is also provided.

Interspecific variation in bristle number on forewings of tiny insects does not influence clap-and-fling aerodynamics

Miniature insects must overcome significant viscous resistance in order to fly. They typically possess wings with long bristles on the fringes and use clap-and-fling mechanism to augment lift. These unique solutions to the extreme conditions of flight at tiny sizes (< 2 mm body length) suggest that natural selection has optimized wing design for better aerodynamic performance. However, species vary in wingspan, number of bristles (n), and bristle gap (G) to diameter (D) ratio (G/D). How this variation relates to body length (BL) and its effects on aerodynamics remain unknown. We measured forewing images of 38 species of thrips and 21 species of fairyflies. Our phylogenetic comparative analyses showed that n and wingspan scaled positively and similarly with body length across both groups, whereas G/D decreased with BL, with a sharper decline in thrips. We next measured aerodynamic forces and visualized flow on physical models of bristled wings performing clap-and-fling kinematics at chord-based Reynolds number of 10 using a dynamically scaled robotic platform. We examined the effects of dimensional (G, D, wingspan) and non-dimensional (n, G/D) geometric variables on dimensionless lift and drag. We found that: (a) increasing G reduced drag more than decreasing D; (b) changing n had minimal impact on lift generation; and (c) varying G/D minimally affected aerodynamic forces. These aerodynamic results suggest little pressure to functionally optimize n and G/D. Combined with the scaling relationships between wing variables and BL, much wing variation in tiny flying insects might be best explained by underlying shared growth factors.

Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade

Recently, the inlet temperatures in gas turbine units have been drastically increased, which extremely affects the lifespan of gas turbine blades. Traditional cooling structures greatly improve the high temperature resistance of the blade; however, these structures scarcely concern both heat transfer and mechanical performances. Lattice structure (LS) can realize these requirements because of its characteristics of light weight, high strength, and porosity. Although the topology of LS is complex, it can be manufactured with the 3D metal printing technology. In this study, an integral optimization method of lattice cooling structure, used at the trailing edge of turbine blades, concerned with heat transfer and mechanical performance, was presented. Firstly, functions between the first-order natural frequency (freq1), elasticity modulus (E), relative density (ρ¯), and Nusselt number (Nu), and the geometric variables of pyramid type LS (PLS) and X-type LS (XLS) were established, and the reliability of these functions was verified. Then, a mathematical optimization model was developed based on these functions which contained two selected optimization problems. Finally, relations among objectives were analyzed; influence law of geometric variables to objectives were discussed, and the accuracy of the optimal LS was proved by experiment and numerical simulation. The optimization results suggest that, compared to the initial LS, Nu increases by 24.1% and ρ¯ decreases by 31% in the optimal LS of the first selected problem, and the Nu increases by 28.8% while freq1 and ρ¯ are almost unchanged in the optimal LS of the second selected problem compared to the initial LS. This study may provide a guidance for functions integration design of lattice cooling structures used at turbine blades based on 3D printing.

Optimization of 3-DoF Manipulators’ Parasitic Motion with the Instantaneous Restriction Space-Based Analytic Coupling Relation

This paper presents a velocity-level approach to optimizing the parasitic motion of 3-degrees of freedom (DoFs) parallel manipulators. To achieve this objective, we first systematically derive an analytical velocity-level parasitic motion equation as a primary step for the optimization. The paper utilizes an analytic structural constraint equation that describes the manipulator’s restriction space to formulate the parasitic motion equation via the task variable coupling relation. Then, the relevant geometric variables are identified from the analytic coupling equation. The Quasi-Newton method is used for the direction-specific minimization, i.e., optimizing either the x-axis or y-axis parasitic motion. The pattern-search algorithm is applied to optimize all parasitic terms from the workspace. The proposed approach equivalently describes the 3-PhRS, 3-PvRS, 3RPS manipulators. Moreover, other manipulators within a similar category can be equivalently expressed by the proposed method. Finally, the paper presents the resulting optimum configurations and numerical simulations to demonstrate the approach.

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Several past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models.

Hack distributions of rill networks and nonlinear slope length–soil loss relationships

Surface flow on rilled hillslopes tends to produce sediment yields that scale nonlinearly with total hillslope length. The widespread observation lacks a single unifying theory for such a nonlinear relationship. We explore the contribution of rill network geometry to the observed yield–length scaling relationship. Relying on an idealized network geometry, we formally develop probability functions for geometric variables of contributing area and rill length. In doing so, we contribute towards a complete probabilistic foundation for the Hack distribution. Using deterministic and empirical functions, we then extend the probability theory to the hydraulic variables that are related to sediment detachment and transport. A Monte Carlo simulation samples hydraulic variables from hillslopes of different lengths to provide estimates of sediment yield. The results of this analysis demonstrate a nonlinear yield–length relationship as a result of the rill network geometry. Theory is supported by numerical modeling, wherein surface flow is routed over an idealized numerical surface and a natural surface from northern Arizona. Numerical flow routing demonstrates probability functions that resemble the theoretical ones. This work provides a unique application of the Scheidegger network to hillslope settings which, because of their finite lengths, result in unique probability functions. We have addressed sediment yields on rilled slopes and have contributed towards understanding Hack's law from a probabilistic reasoning.

Embodied Energy Optimization of Buttressed Earth-Retaining Walls with Hybrid Simulated Annealing

The importance of construction in the consumption of natural resources is leading structural design professionals to create more efficient structure designs that reduce emissions as well as the energy consumed. This paper presents an automated process to obtain low embodied energy buttressed earth-retaining wall optimum designs. Two objective functions were considered to compare the difference between a cost optimization and an embodied energy optimization. To reach the best design for every optimization criterion, a tuning of the algorithm parameters was carried out. This study used a hybrid simulated optimization algorithm to obtain the values of the geometry, the concrete resistances, and the amounts of concrete and materials to obtain an optimum buttressed earth-retaining wall low embodied energy design. The relation between all the geometric variables and the wall height was obtained by adjusting the linear and parabolic functions. A relationship was found between the two optimization criteria, and it can be concluded that cost and energy optimization are linked. This allows us to state that a cost reduction of €1 has an associated energy consumption reduction of 4.54 kWh. To achieve a low embodied energy design, it is recommended to reduce the distance between buttresses with respect to economic optimization. This decrease allows a reduction in the reinforcing steel needed to resist stem bending. The difference between the results of the geometric variables of the foundation for the two-optimization objectives reveals hardly any variation between them. This work gives technicians some rules to get optimum cost and embodied energy design. Furthermore, it compares designs obtained through these two optimization objectives with traditional design recommendations.

IMPROVEMENT IN KERATOCONUS DIAGNOSIS USING MORPHO-GEOMETRIC VARIABLES WITH RNN NETWORKS

There is an eye disease called Keratoconus (KC) which has potential to cause visual acuity loss; hence, it can be considered as disability due to its severity. There are some limitations in current method in detecting cornea region’s boarder edge. Primary objective for the paper need to identify the structural description of disease’ asymmetry with the help of Morpho-geometric parameters relates with the keratoconous eyes along by means of slight visual control. It also includes the application of Recurrent Neural Network (RNN) analysis which is sort of Neural Network in which previous step’s output are sent to present step as an input. In order to determine most prominent correlation, Stepwise Discriminant Function Analysis is used in analyzing output. The Prominent correlation was identified between thinnest point in the anterior deviation and thinnest point in the posterior deviations of minor keratoconic cases. MATLAB R2014 software is used to implement the framework and analyses of simulation results were performed.