Collision Rate Research Articles

Close Encounters of Wide Binaries Induced by the Galactic Tide: Implications for Stellar Mergers and Gravitational-wave Sources

A substantial fraction of stars can be found in wide binaries with projected separations between ∼102 and 105 au. In the standard lore of binary physics, these would evolve as effectively single stars that remotely orbit one another on stationary Keplerian ellipses. However, embedded in their Galactic environment, the low binding energy of wide binaries makes them exceptionally prone to perturbations from the gravitational potential of the Milky Way and encounters with passing stars. Employing a fully relativistic N-body integration scheme, we study the impact of these perturbations on the orbital evolution of wide binaries along their trajectory through the Milky Way. Our analysis reveals that the torques exerted by the Galaxy can cause large-amplitude oscillations of the binary eccentricity to 1 − e ≲ 10−8. As a consequence, the wide binary members pass close to each other at periapsis, which, depending on the type of binary, potentially leads to a mass transfer or collision of stars or to an inspiral and subsequent merger of compact remnants due to gravitational-wave radiation. Based on a simulation of 105 wide binaries across the Galactic field, we find that this mechanism could significantly contribute to the rate of stellar collisions and binary black hole mergers as inferred from observations of luminous red novae and gravitational-wave events by LIGO/Virgo/Kagra. We conclude that the dynamics of wide binaries, despite their large mean separation, can give rise to extreme interactions between stars and compact remnants.

Impact of coagulation characteristics on the aggregation of microplastics in upper-ocean turbulence

The dynamics and aggregation of microplastics in marine environments are investigated through high-fidelity direct numerical simulations with Lagrangian point-particle tracking. The properties of microplastics and biogenic particles, including size, density, and concentration, align with scenarios typical of seawater systems. The stickiness nature of microplastics, induced by biofilm formation (biofouling), is modeled through coagulation efficiency (stickiness parameter), which represents the probability of aggregation following a collision event. Two main aspects are at the core of the present work: analyzing the mechanisms of collision and coalescence between microplastics and biogenic particles, along with their spatial distribution, and characterizing the emerging aggregates. The results indicate that particles stickiness, concentration and (especially) size impact on the collision and coalescence rates. Furthermore, microplastics exhibit a strong tendency to accumulate near biogenic particles, leading to the creation of hetero-aggregates whose tendency to sink supports the general hypothesis of “missing microplastics”. Particularly, in cases where microplastics and biogenic particles are evenly concentrated, microplastics primarily contribute to the formation of aggregates. The stickiness mainly determines the most complex and large aggregates, which are less than 1% of the total.

Examination of particle motion in fluidized bed flotation columns: Effect of particle shape

The critical analysis of particle dynamics within the flow field is vital for the optimization and enhancement of new fluidized bed flotation columns. Prior research has centered on the kinetic properties of auxiliary fluidized particles (glass spheres) in these columns [1]. However, in real-world applications, these particles undergo wear and transform into irregular shapes. This investigation seeks to explore the motion characteristics of particles of various geometries in a three- phase gas-liquid-solid environment. By quantifying the velocities of particles with different shapes, we aim to improve the evaluation of their dispersion and collision behavior with the column walls. First, the particle concentration distribution within the fluidized zone is continuously monitored through pressure sensors at various axial positions, allowing us to examine the concentration patterns of particles from different shape categories under various operational conditions. Second, the motion dynamics of particles within the fluidization zone are studied using high-speed camera techniques. Particle velocities are determined by correlating tracer particle velocities with black markers for precise measurement. To evaluate the frequency of particle-wall collisions, a modified collision model is applied to ascertain collision rates and to investigate the influence of shape parameters on such interactions. Lastly, the normal and tangential restitution coefficients are measured based on the trajectories of particles before and after collisions with the walls.

Operation of the Belle II imaging Time-Of-Propagation (iTOP) detector before and after the first long shutdown

The iTOP detector is a Cherenkov detector specialized on particle identification at Belle II. The SuperKEKB accelerator collides electrons and positrons with a design luminosity of 6⋅10351cm2s. In order to exploit the high collision rate Belle II has a trigger rate of up to 30 kHz. The iTOP detector uses quartz bars as the source of Cherenkov photons. The photons are reflected inside the bars until they hit photomultipliers installed at one end. The spatial distribution and precise arrival times of the detected photons are used to reconstruct the Cherenkov angle and particle flight time. To achieve a good pion–kaon separation the photon arrival times have to be measured with a resolution of 100 ps. Microchannel plate photomultipliers together with dedicated high-speed electronics for 2.7 GSa/s waveform sampling in 8192 channels are used to achieve this requirement. After four years of operation, the experiment entered its first long shutdown phase in 2022. Aging components of iTOP were exchanged and the detector was prepared for data-taking at increasing luminosities and backgrounds after the long shutdown. In this talk the design of the iTOP detector will be shown and experience and results from operation will be discussed together with an outlook on future running conditions.

The impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania.

The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID-19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. Using monthly data from the Lithuanian Road Police Service for January 2004 to December 2022, we performed interrupted time-series analyses using a generalized additive model to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities. In Lithuania, the COVID-19-related lockdowns occurred from March 2020 to June 2020 and from November 2020 to June 2021. Although overall rates for traffic collisions and injuries decreased during the COVID-19-related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol-related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol-related traffic injuries. The association between the lockdowns and alcohol-related traffic fatalities was not statistically significant. The COVID-19-related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol-related traffic collisions and injuries.

Evaluation of advanced emergency braking systems in drowsy driving-related real-world truck collisions.

The effectiveness of advanced emergency braking systems (AEBS) in preventing drowsy driving-related truck collisions remains unclear. We aimed to evaluate the damage-mitigation effect of AEBS on drowsy driving-related collisions involving large trucks using collision rate and damage amount. Data collected by a Japanese transportation company from 1,699 collisions involving 31,107 large trucks over 7 years were analyzed post-hoc. The collision rate (number of trucks with collisions/total number of trucks) and damage amount (total amount of property damage and personal injury) were compared based on whether the collisions were caused by drowsy or non-drowsy driving and whether the trucks were equipped with AEBS or not. For all and non-drowsy driving-related collisions, the collision rate for the 12,887 trucks with AEBS (1.62 and 1.20 collisions/truck/7 years, respectively) was significantly lower than that for the 18,220 trucks without AEBS (1.94 and 1.56 collisions/truck/7 years, respectively) (p=0.04 and p=0.008, respectively). However, for drowsy driving-related collisions, the collision rate did not significantly differ between trucks with and without AEBS. The damage amount in neither type of collision (drowsy vs. non-drowsy) significantly differed between trucks with and without AEBS. Regarding the collision rate of large trucks, AEBS was effective in non-drowsy driving-related collisions, but not in collisions involving drowsy driving. The damage amount was not mitigated for trucks with and without AEBS regardless of the collision type. The limited effect of AEBS for damage-mitigation suggests the need for combined use with other safety-support systems that intervene in driving operations.

40 MHz readout of CMS silicon modules in a high intensity beam

In order to maintain its outstanding performance under the challenging conditions brought by the high-luminosity LHC, the CMS collaboration is preparing the production of a new outer tracker detector. The upgraded detector modules will feature two silicon sensors and the ability of reading out correlated clusters, or stubs, compatible with high transverse momentum particles at the full 40 MHz collision rate. With the detector design being finalised and mass production planned to start during the second half of 2024, the scalability of the readout system and the study of the commissioning and characterisation of the detector in realistic conditions are ever-more pressing.In this context, a joint beam-test was organised in collaboration with the MUonE collaboration where twelve modules were placed in an asynchronous muon beam line reaching particle rates of about 50 MHz, with the full stub stream being recorded to disk triggerless. The experiment and read-out chain is outlined, the commissioning procedures and operational challenges discussed and resulting system performance are presented.

Path Planning for Floor Cleaning Robots Based on MATLAB Simulink

The increasing popularity of robotic vacuum cleaners in households in recent years has established them as indispensable devices in many homes and has also demonstrated their potential in various other domains. Nevertheless, numerous robotic sweepers available in the market exhibit certain constraints, including repetitive sweeping of the same area, collisions, or entrapment by obstacles. These limitations restrict their effectiveness in specific settings, such as hospitals, where comprehensive cleaning is essential. In an effort to enhance the efficiency and effectiveness of robotic sweepers, a research project was undertaken to investigate the feasibility of navigating a two-dimensional environment utilizing plannerAStar, a path planning algorithm in MATLAB. The study findings indicated a direct correlation between the quantity of goals established through the A-planner and the percentage of independent obstacles effectively circumvented, as well as the area traversed in a distinct "side-to-side" configuration. The enhanced pattern variant exhibited greater consistency and coverage in an environment characterized by walls and corridors. Improved path planning algorithms have been developed utilizing the plannerAStar function in MATLAB Simulink to address issues related to collisions and obstructions encountered by swinging robots in real-world scenarios. Simulation results demonstrate that the path planning algorithm utilizing plannerAStar significantly decreases the path length by 34%, reduces the number of collisions by 75%, and lowers the collision rate by 10% in comparison to the fundamental randomized algorithm. The findings indicate that utilizing the plannerAStar algorithm for path planning is anticipated to notably decrease both redundant collisions and obstacle collisions for the robot, consequently enhancing the robot's operational efficiency.

Rotational dynamics of CNCN by p-H2 and o-H2 collision at interstellar temperatures.

The rotational dynamics of isocyanogen (CNCN) is studied for its collision with para (p-) and ortho (o-) hydrogen (H2) in the temperature range of 1-100 K. These temperatures correspond to the cold dense molecular clouds in the interstellar medium where molecular hydrogen is the primary collider. An abinitio 4D potential energy surface (PES) is constructed keeping the two molecules under rigid rotor approximation. The PES is generated using the CCSD(T)-F12b/AVTZ level of theory. The 4D PES is further fitted into a neural network (NN) model, which can augment the surface and account for missing data points within spectroscopic accuracy. This NN-fitted PES is then expanded over a bispherical harmonics function to get radial terms, which are expressed into analytic functions. Thereafter, the cross sections (σ) are computed for rotational transitions of CNCN (j → j') using the close-coupling and centrifugal sudden methods for both p-H2 (jc = 0) and o-H2 (jc = 1) collision till 194 cm-1. In addition, p-H2 (jc = 0, 2) cross sections are also computed using the centrifugal sudden approximation method. The collisional rates are achieved by taking the Boltzmann distribution of σ over the translational energy of H2 till 100K. Finally, the CNCN-H2 rates are compared to CNCN-He and NCCN-H2 collisional rates. Comparing even and odd transitions for the CNCN-H2 rates show a propensity toward higher rates for even transitions especially for o-H2 collisions considering low-order transitions.

Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay

Deep Reinforcement Learning (DRL) models play a fundamental role in autonomous driving applications; however, they typically suffer from sample inefficiency because they often require many interactions with the environment to learn effective policies. This makes the training process time-consuming. To address this shortcoming, Prioritized Experience Replay (PER) has proven to be effective by prioritizing samples with high Temporal-Difference (TD) error for learning. In this context, this study contributes to artificial intelligence by proposing a sample-efficient DRL algorithm called Episodic-Guided Prioritized Experience Replay (EPER). The core innovation of EPER lies in the utilization of an episodic memory, dedicated to storing successful training episodes. Within this memory, expected returns for each state–action pair are extracted. These returns, combined with TD error-based prioritization, form a novel objective function for deep Q-network training. To prevent excessive determinism, EPER introduces exploration into the learning process by incorporating a regularization term into the objective function that allows exploration of state-space regions with diverse Q-values. The proposed EPER algorithm is suitable to train a DRL agent for handling episodic tasks, and it can be integrated into off-policy DRL models. EPER is employed for traffic navigation through scenarios such as highway driving, merging, roundabout, and intersection to showcase its application in engineering. The attained results denote that, compared with the PER and an additional state-of-the-art training technique, EPER is superior in expediting the training of the agent and learning a more optimal policy that leads to lower collision rates within the constructed navigation scenarios.

Placing the leading limb closer to an obstacle reduces collision of the trailing limb: an investigation in a virtual environment.

When walking and stepping over an obstacle of a certain height, tripping occurs more frequently with the trailing limb than the leading limb. The present study was designed to address whether collisions involving the trailing limb can be improved with experimental manipulation of the placement of the leading limb after stepping over an obstacle. We used an immersive, virtual obstacle-crossing task to ensure that the collision was not improved simply due to the experience of physical collision with an obstacle. Fourteen young participants (12 males and 2 females, 28.7 ± 3.5 years) were required to walk and step over a virtual horizontal pole under one of four conditions. In three conditions, participants were required to place their leading foot on a square target located along their walking path after crossing the obstacle. The target was positioned so that it was relatively close to the obstacle (10 cm from the obstacle, referred to hereafter as the closer condition), at a position that would naturally be stepped on in successful trials without a collision (20 cm from the obstacle, the middle condition), or relatively far from the obstacle (40 cm from the obstacle, the farther condition). For the fourth condition, participants were free to select where they would step after stepping over the obstacle (the control condition). The results showed that the collision rate of the trailing limb was significantly lower under the closer condition than under the other three conditions. Compared to the control condition, under the closer condition the movement of the trailing limb was modified so that obstacle crossing was performed at approximately the moment when the height of the toe of the trailing limb was higher, and the walking speed was slower. These findings suggest that placing the foot of the leading limb closer to the obstacle after crossing the obstacle may ensure safe obstacle avoidance by the trailing limb.

Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions.

Event generator for jet tomography in electron-ion collisions

We develop the first event generator, the electron-heavy-ion-jet-interaction-generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering undergo multiple collisions with the nuclear target. The collision rate is proportional to the transverse-momentum-dependent (TMD) gluon density in the nucleus, which is given by a simple model inspired by the physics of gluon saturation. Medium-modified QCD splitting functions within the higher-twist (HT) and generalized higher-twist (GHT) frameworks are utilized to simulate parton showering in the nuclear medium that takes into account the non-Abelian Landau-Pomeranchuck-Midgal interference effect. Employing eHIJING, we revisit hadron production in semi-inclusive deep inelastic scattering (SIDIS) as measured by EMC, HERMES, and recent CLAS experiments. eHIJING with both GT and GHT frameworks gives reasonably good descriptions of these experimental data. Predictions for experiments at the future electron-ion colliders are also provided. It is demonstrated that future measurements of the transverse momentum broadening of single hadron spectra can be used to map out the two-dimensional kinematic (Q2, xB) dependence of the jet transport coefficient q^ in cold nuclear matter. Published by the American Physical Society 2024

An FPGA-based parallel deconvolution application envisaging high-energy calorimeters operating under severe pile-up conditions

Signal pileup may arise when the system response time is larger than what would be required by the event rate in the application. In this condition, information superposition occurs, and signal estimation is deteriorated due to such information distortion. For online applications, mitigating such a pileup distortion usually requires embedded digital signal processing, which often involves FPGA-based designs when high event rates are a concern. Recently, high-energy particle experiments started facing an unprecedented increase in pileup conditions, which demanded new approaches for signal estimation. This work proposes a dedicated parallel signal processing system embedded on an FPGA platform that implements different filtering techniques for online energy reconstruction in calorimeters (energy measurement) operating under severe pileup conditions. Each designed filter is an inverse approximation of the calorimeter readout channel's impulse response, performing deconvolution of the incoming signals. Such a deconvolution approach for energy estimation was recently introduced, and the proposed filters were implemented in this work. Evaluation of the digital system design was performed using a data set that considers a typical high-energy calorimeter front-end response, as high-luminosity particle collider experiments produce high pileup distortions in calorimeter signals when processing the subproducts of the collisions. As the Large Hadron Collider (LHC) provides the most demanding conditions in terms of signal pileup, the acquisition system of the proposed solution is synchronous with its collision rate (40 MHz). The results showed that the proposed implementation may be applied as a preprocessing (pileup reduction) step in calorimeter instrumentation and could reduce the signal pileup within the fast response time required by such experiments.

Deep Reinforcement Learning-based Collaborative Multi-UAV Coverage Path Planning

Abstract The coverage path planning problem has gained significant attention in research due to its wide applicability and practical value in various fields such as logistics and distribution, smart homes, and unmanned vehicles. This paper focuses on studying the coverage path planning problem under multi-UAV collaboration to maximize the coverage of the mission area within a given time. To address this problem, we propose a multi-objective optimization model and reformulate it with the framework of Decentralized Partially Observable Markov Decision Process (Dec-POMDP). We then employ a multi-agent deep reinforcement learning (MADRL) method to solve the problem. Specifically, we introduce the ε—Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (ε—MADT3), which incorporates an exploration coefficient based on MATD3. This coefficient gradually decays with the number of iterations, allowing for a balance between exploration and exploitation. Numerous simulation results demonstrate that ε—MADT3 outperforms the baseline algorithm in terms of coverage rate and number of collisions.

How caged motion in the contact layer enhances thermal tunneling across a liquid/solid interface

Ever more powerful and densely packed chips for applications like cryptocurrency mining and artificial intelligence generate such enormous heat fluxes that designers are pivoting from gas to liquid cooling to forestall damage from thermal runaway. Even with optimal flow patterns, however, the intrinsic thermal boundary resistance at the liquid/solid (L/S) interface poses an additional source of thermal impedance. There is a lingering misconception in the field that the higher the liquid contact density, the more frequent the L/S collision rate and the smaller the thermal slip length. Here we present an insightful counterexample based on nonequilibrium molecular dynamics simulations of a simple liquid confined between two face centered cubic crystals at different temperatures aligned with the [001], [011] or [111] facet plane. Measurements of various static and dynamic quantities of the contact layer reveal the ways in which long-range order, anisotropy of the L/S potential, and correlated motion act to reduce thermal boundary resistance. Systems with the smallest thermal slip length exhibit two distinct features: 2D caged motion with stringlike alignment of liquid particles, unlike that observed in glassy systems, and larger nonergodicity parameter with shorter, not longer, caging times. This trapping and release mechanism suggests a paradigm for the design of L/S interfaces to maximize thermal exchange across a classical L/S interface. Published by the American Physical Society 2024

Six-Axis Robotic Gripping Control Algorithm Based on Deep Learning

Due to several complex factors such as the type, size, and shape of target object, vision-assisted robot grasping technology still faces serious challenges. In this research, a deep learning-based robot hand vision grasping algorithm was developed considering semi-structural environmental constraints. The proposed algorithm could build a deep learning network on the basis of the desired object, perform object recognition, category classification and position judgment, and complete robot hand-grasping tasks. The obtained experimental results demonstrated that the proposed algorithm effectively solved the problem of recognizing and classifying multi-category objects in a semi-structured environment, improving recognition rate and grasping rate and reducing collision rate.

Diversity and spatial genetic structure of the common opossum (Didelphis marsupialis) in an urban matrix of Northwestern Colombia

The common opossum (Didelphis marsupialis) is a marsupial widely distributed in the Neotropics, where it lives in urbanized environments. The apparent scarcity and fragmentation of available habitat, and high rates of vehicle collisions, may represent barriers to dispersal. To assess the functional connectivity of this species and its potential use as a model in urban ecology, we evaluated the structure and genetic diversity of opossums in the metropolitan area of the Aburrá Valley (AMVA), Colombia, using sequences of the mitochondrial Cytochrome B (Cytb) gene and nine autosomal microsatellite loci. Although Cytb presented low levels of variation, microsatellite markers revealed high genetic diversity (He = 0.852 and Ho = 0.698). The geographic distribution of mitochondrial lineages and a spatial principal component analysis (sPCA) of microsatellites showed the absence of genetic structure and effective barriers to dispersal of opossums in the study area. This scenario suggests that hostile landscapes such as those in urban environments may be suitable enough to allow the effective connectivity of some species such as the common opossum and others with high dispersal ability in urbanized areas. Nonetheless, it is necessary to evaluate the impact of releases of rescued fauna made by the environmental authority on the population structure of urban opossums.

Rotational state-to-state transition rate coefficients for H2O + H2O collisions at nonequilibrium conditions

The goal is to develop a database of rate coefficients for rotational state-to-state transitions in H$_ $O + H$_ $O collisions that is suitable for the modeling of energy transfer in nonequilibrium conditions, in which the distribution of rotational states of H$_ $O deviates from local thermodynamic equilibrium. A two-temperature model was employed that assumed that although there is no equilibrium between all possible degrees of freedom in the system, the translational and rotational degrees of freedom can be expected to achieve their own equilibria independently, and that they can be approximately characterized by Boltzmann distributions at two different temperatures, $T_ kin $ and $T_ rot Upon introducing our new parameterization of the collisional rates, taking into account their dependence on both $T_ kin $ and $T_ rot $, we find a change of up to 20<!PCT!> in the H$_ $O rotational level populations for both ortho and para-H$_ $O for the part of the cometary coma where the nonequilibrium regime occurs.

Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.

In this manuscript, we delve into the realm of lattice ordered complex linear diophantine fuzzy soft set, which constitutes an invaluable extension to the existing Fuzzy set theories. Within this exploration, we investigate basic operations such as and , together with their properties and theorems. This manuscript is more amenable in two ways, i.e., it enables real-life problems involving parametrization tool and applications with an existing order between the components of the parameter set based on the preference in the complex frame of reference. Adaptive cruise control (ACC) is a system designed for maintaining distance between two vehicles and to sustain a manually provided input speed. The purpose of cars with ACC is to avoid a collision that frequently happens nowadays, thereby improving road safety regulations amidst rising collision rates. The fundamental aim of this manuscript is to prefer an applicable car with ACC together with its latest model by defining a peculiar postulation of lattice ordered complex linear diophantine fuzzy soft set . Emphasizing real-life applicability, we illustrate the effectiveness and validity of our suggested methodology in tackling current automotive safety concerns, providing useful guidance on reducing challenges related to contemporary driving conditions.