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A common element in physiological flow networks, as well as most domestic and industrial piping systems, is a T junction that splits the flow into two nearly symmetric streams. It is reasonable to assume that any particles suspended in a fluid that enters the bifurcation will leave it with the fluid. Here we report experimental evidence and a theoretical description of a trapping mechanism for low-density particles in steady and pulsatile flows through T-shaped junctions. This mechanism induces accumulation of particles, which can form stable chains, or give rise to significant growth of bubbles due to coalescence. In particular, low-density material dispersed in the continuous phase fluid interacts with a vortical flow that develops at the T junction. As a result suspended particles can enter the vortices and, for a wide range of common flow conditions, the particles do not leave the bifurcation. Via 3D numerical simulations and a model of the two-phase flow we predict the location of particle accumulation, which is in excellent agreement with experimental data. We identify experimentally, as well as confirm by numerical simulations and a simple force balance, that there is a wide parameter space in which this phenomenon occurs. The trapping effect is expected to be important for the design of particle separation and fractionation devices, as well as used for better understanding of system failures in piping networks relevant to industry and physiology.

The hippocampal formation is believed to be critical for the encoding, consolidation, and retrieval of episodic memories. Yet, how these processes are supported by the anatomically diverse hippocampal networks is still unknown. To examine this issue, we tested rats in a hippocampus-dependent delayed spatial alternation task on a modified T maze while simultaneously recording local field potentials from dendritic and somatic layers of the dentate gyrus, CA3, and CA1 regions by using high-density, 96-site silicon probes. Both the power and coherence of gamma oscillations exhibited layer-specific changes during task performance. Peak increases in the gamma power and coherence were found in the CA3-CA1 interface on the maze segment approaching the T junction, independent of motor aspects of task performance. These results show that hippocampal networks can be dynamically coupled by gamma oscillations according to specific behavioral demands. Based on these findings, we propose that gamma oscillations may serve as a physiological mechanism by which CA3 output can coordinate CA1 activity to support retrieval of hippocampus-dependent memories.

We investigate the role played by curve singularity germs in the enumeration of inflection points in families of curves acquiring singular members. Let

Droplets of one liquid suspended in a second, immiscible liquid move through a microfluidic device in which a channel splits into two branches that reconnect downstream. The droplets choose a path based on the number of droplets that occupy each branch. The interaction among droplets in the channels results in complex sequences of path selection. The linearity of the flow through the microchannels, however, ensures that the behavior of the system can be reversed. This reversibility makes it possible to encrypt and decrypt signals coded in the intervals between droplets. The encoding/decoding device is a functional microfluidic system that requires droplets to navigate a network in a precise manner without the use of valves, switches, or other means of external control.

Transformer-based models have demonstrated outstanding performance in trajectory prediction; however, their complex architecture demands substantial computing power, and their performance degrades significantly in long-term prediction. A transformer model was developed to predict vehicle trajectory in urban low-speed T-intersections. Microscopic traffic simulation data were generated to train the trajectory-prediction model; furthermore, validation data focusing on atypical scenarios were also produced. The appropriate loss function to improve prediction accuracy was explored, and the optimal input/output sequence length for efficient data management was examined. Various driving-characteristics data were employed to evaluate the model’s generalization performance. Consequently, the smooth L1 loss function showed outstanding performance. The optimal length for the input and output sequences was found to be 1 and 3 s, respectively, for trajectory prediction. Additionally, improving the model structure—rather than diversifying the training data—is necessary to enhance generalization performance in atypical driving situations. Finally, this study confirmed that the additional features such as vehicle position and speed variation extracted from the original trajectory data decreased the model accuracy by about 21%. These findings contribute to the development of applicable lightweight models in edge computing infrastructure to be installed at intersections, as well as the development of a trajectory prediction and accident analysis system for various scenarios.

The main objective of this paper is to evaluate and compare the operational efficiency of a conventional signalized T-intersection with an unconventional Continues Green T-intersection under different congestion levels. The analysis was performed using Synchro.8 micro-simulation software. A total of 48 hypothetical scenarios, 24 scenarios for each design, were created by changing the approach volumes and turning percentages on the major / minor intersecting roadways to reflect different levels of congestion that may occur on any urban intersection. Total intersection delay, Level of Service, maximum queue length and volume-to-capacity ratio (v/c) were the measures of effectiveness used for comparison purposes. These performance measures were selected because they demonstrated the overall efficiency of the intersection design. The simulation results showed that the Continuous Green T-intersection operates the best under stable traffic conditions and that it is not an effective solution for signalized T-intersections under heavy traffic volume.

We define a class of random measures, spatially independent martingales, which we view as a natural generalization of the canonical random discrete set, and which includes as special cases many variants of fractal percolation and Poissonian cut-outs. We pair the random measures with deterministic families of parametrized measures

This study explores the contributing factors that influence bicyclist injury severity at three types of intersection: roundabouts, crossroads, and T-junctions. Using bicycle-involved crash data in the UK over nine years (from 2009 to 2017), the bicyclist injury severity (with three severity levels: fatal injury, serious injury, and slight injury) was estimated using the generalized ordered logit (GOL) model and partial proportional odds (PPO) model. The marginal effects of each explanatory variable were computed to investigate the impacts on bicyclist injury severity occurring probabilities. A wide range of variables potentially affecting injury severity was considered, including bicyclist characteristics, intersection characteristics, environmental conditions, bicyclist movement and location preceding the crash, and types of collisions. Our findings show that the PPO model outperforms the GOL model for analyzing the factors that affect the bicyclist injury severity at intersections. The factors that affect cycling safety at various intersections show enormous differences. Specifically, nine variables have significant impacts on bicyclist injury severity at those three types of intersections. And there are only two variables, four variables, and eleven variables that have significant impact on bicyclist injury severity at roundabouts, crossroads, and T-junctions, respectively. The findings of this study can help decision makers better understand the spatial heterogeneity of the factors that influence the bicyclist injury severity at various intersections.

Singularities of non-Hermitian systems typified by exceptional points (EPs) are critical for understanding non-Hermitian topological phases and trigger many intriguing phenomena. However, it remains unexplored what happens when EPs meet one another. Here, in a typical four-level model with both touching and crossing intersections of EP hypersurfaces, we report the interconversion mechanisms between EPs of different orders. By examining both the eigenvalues and eigenvectors, we show analytically that all EPs of higher orders are formed at the touching intersections of two different types of EP hypersurfaces of lower orders. Contrarily, the crossing intersection of EP structures lowers the order of EPs. The mechanisms of the increase and decrease in defectiveness discovered here are expected to hold for EPs of any order in various non-Hermitian systems, providing a comprehensive understanding of EPs and inspiration toward advanced applications such as biosensing and information processing.

Let We do it simplicially in the setting of a filtered version of face sets, also called simplicial sets without degeneracies, in the sense of C.P. Rourke and B.J. Sanderson. We define perverse local systems over filtered face sets and intersection cohomology with coefficients in a perverse local system. In particular, as announced above when For that, we construct a functor from the category of filtered face sets to a category of perverse commutative differential graded