High Collisions Research Articles

DDFRG2: Double-Differential FRaGmentation model for pion production in high energy nuclear collisions

A set of Double-Differential FRaGmentation (DDFRG) models for production of fragments from nucleon–nucleus and nucleus–nucleus collisions, relevant to space radiation, is currently being developed. DDFRG1 was a previous model developed for production of protons and light ions. A new model for pion production is developed in the present work and is called DDFRG2, which is based upon thermal production of pions from a central fireball source. The Lorentz-invariant double-differential cross section is calculated in the central fireball frame, and is Lorentz transformed to the laboratory frame, which results in a closed-form analytic formula involving no numerical integration, and which can be run very efficiently in radiation transport codes. The Lorentz-invariant double-differential cross section is then integrated over angle to give the single-differential energy spectral distribution. The calculated Lorentz-invariant double-differential cross sections are compared to an extensive data set containing over 1000 data points. Agreement between model calculations and data is found to be excellent.

Evolution of eccentricities induced by geometrical and quantum fluctuations in proton-nucleus collisions

We compute the azimuthal eccentricities arising from initial stage fluctuations in high energy proton-nucleus collisions at proper times $\tau \geq 0^+$. We consider two sources of fluctuations, namely the geometrical structure of the proton and the fluctuation of color fields carried by both proton and nucleus. Describing these effects with Gaussian models allows us to analytically calculate the one- and two-point correlators of energy density, from which the eccentricities are obtained. We compute the proper time evolution of these quantities by approximating the Glasma dynamics in terms of linearized Yang-Mills equations, which we solve by assuming free field propagation and adopting the dilute-dense limit.

Subterranean mMTC in Remote Areas: Underground-to-Satellite Connectivity Approach

Wireless underground sensor networks (WUSNs) promise to deliver substantial social and economic benefits across different verticals. However, many of the relevant application scenarios are located in remote areas with no supporting infrastructure available. To address this challenge, we conceptualize in this study the underground direct-to-satellite (U-DtS) connectivity approach, implying the reception of the signals sent by the underground massive machine-type communication (mMTC) sensors by the gateways operating on the low Earth orbit satellites. We start by discussing the two alternative architectures for enabling such networks and underline their features and limitations. Then, we employ simulations to model a smart-farming application scenario for studying the feasibility and performance of UDtS mMTC based on LoRaWAN technology, with the focus on the effect of the parameters including the burial depths, soils characteristics, and communication settings. Our results reveal that the LoRa modulation fails to compete with the underground propagation losses and the high packets collision rate. However, the long range-frequency hopping spread spectrum (LR-FHSS) modulation is perspective for U-DtS networks and may enable connectivity for the subsurface nodes located at a depth of several dozens centimeters. Finally, we pinpoint the potential challenges and the research directions for future U-DtS studies.

A Novel MAC-Based Authentication Scheme (NoMAS) for Internet of Vehicles (IoV)

The fully dynamic dense environment of the Internet of Vehicles (IoV) and conditions like high traffic jams, collisions, and uneven road conditions increase the communication messages. Consequently, the computation and communication costs of the IoV network are increased. Therefore, IoV needs a lightweight and efficient security solution along with effective communications. Existing state-of-the-art schemes in IoV are generally based on cryptographic methods that use session keys, public keys, elliptic curves, and Message Authentication Codes (MAC). In contrast to the other schemes, MAC-based schemes provide feasible security solutions having high performance with less overhead. The existing MAC-based authentication schemes are vulnerable to the key disclosure issue along with some security attacks such as side-channel attacks, Distributed Denial of Services (DDoS), etc. Due to the side channel attack, a data leakage problem occurs, which is a serious concern in security and privacy facets. We propose a novel MAC-based authentication scheme (NoMAS) that mitigates the above-discussed challenges and provides all the benefits of MAC-based schemes. The NoMAS scheme offers a solution for key disclosure issues by providing Hard Key and Soft Key Updates (HKU and SKU) and data leakage problems using encryption along with high performance. It reduces the computational overhead maximum to 99.60% and the communication overhead maximum to approximately 81% compared to the existing schemes. In the security analysis, We have provided the formal security proof using BAN logic and the ProVerif tool and verified the correctness of the scheme.

Influence of magnetic field gradient on the capacitive argon discharge at 8 MHz and 40 MHz

A one-dimensional implicit particle-in-cell/Monte Carlo collision model is used to study the effects of magnetic field gradients on the capacitively coupled argon plasma at 8 MHz and 40 MHz. The magnetic field strength at the powered electrode is fixed at 10 G, while varies from 30 to 100 G at the grounded electrode. The simulations show that the magnetic field with variable gradient can produce controllable asymmetry in the plasma density and ion flux profiles to each electrode. Increasing the magnetic field gradient will generate a significant dc self-bias, which results in a large ion bombardment energy at the powered electrode. The magnetic field gradients have been demonstrated to be an approach to create the dc self-bias and also effectively improve the plasma density. It is also found that at a higher frequency of 40 MHz, the dc self-bias voltage decreases, due to the fact that high collision rate of electrons with background gas will disturb the cyclotron motion of electrons, so the effect of the magnetic field is weakened. As a result, the ability to independently control ion energy and flux is weakened.

Effects of High-collision Plasma on Radiation Protection of Human Brain

Objective: The purpose of this paper is to find a reasonable composite structure of electromagnetic protection device, especially the distribution characteristics of plasma parameters and operation conditions. Methods: Due to its high collision frequency, the high-pressure discharge plasma can be usedas a filling medium to take the electromagnetic protection effect. The strategies and algorithmsare presented to solve the propagation characteristics of electromagnetic wave (EMW) in plasma with complex dielectric constant. When the real and imaginary part of permittivity are of the same order of magnitude, the imaginary part value will impact on the feature of plasma dielectric constant, also affect the behavior of propagation of EMW. The propagation process of EMW in virtual human brain (VHB) model is investigated by finite difference time domain method. By optimizing the layout of plasma layer and air layer, an effective phase regulator is constructed, and the reflected wave amplitude is greatly reduced. Results: The simulation results show that plasma makes the amplitude of the electromagnetic fields in VHB decrease to different degrees. their maximum average decrease is about 25% and the corresponding reduction of SAR value is more than 40% in the frequency range of 1.8-3.6GHz. Conclusion: The existence of plasma can make the scalp and muscle possess the excellent performance in resisting the incident of EMW, so that the amplitude of the electromagnetic field inside the brain body is small, so as to effectively protect the brain tissue. These research results have good reference value and engineering applicability for the structure design of protection equipment with high frequency of EMW.

SCAP SigFox: A Scalable Communication Protocol for Low-Power Wide-Area IoT Networks

The Internet of Things (IoT) is a new future technology that is aimed at connecting billions of physical-world objects to the IT infrastructure via a wireless medium. Many radio access technologies exist, but few address the requirements of IoT applications such as low cost, low energy consumption, and long range. Low-Power wide-area network (LPWAN) technologies, especially SigFox, have a low data rate that makes them suitable for IoT applications, especially since the lower the data rate, the longer the usable distance for the radio link. SigFox technology achieves as a main objective network reliability by striving for the successful delivery of data messages through redundancy. Doing so results in one of the SigFox weaknesses, namely the high collision rate, which questions SigFox scalability. In this work, we aimed at avoiding collisions by changing SigFox's Aloha-based medium access protocol to TDMA and by using only orthogonal channels while removing redundancy. Consequently, every node sends a single copy of the data message on a given orthogonal channel in a specific time slot. To achieve this, we implemented a slot- and channel-allocation protocol (SCAP) on top of SigFox. In other words, our goal was to improve SigFox's scalability by implementing two mechanisms: time slot allocation and channel allocation. Performance analysis was conducted on large networks with sizes ranging from 1000 to 10,000 nodes to evaluate both technologies: the original SigFox and SCAP SigFox. The simulation results showed that SCAP SigFox highly reduced the probability of collision and energy consumption when compared to the original SigFox. Additionally, SCAP SigFox had a greater throughput and packet delivery ratio (PDR).

Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting

Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN-based designs to better model human motion in crowds. Despite superior performance in reducing distance-based metrics, current networks fail to output socially acceptable trajectories, as evidenced by high collisions in model predictions. To counter this, we introduce SGANv2: an improved safety-compliant SGAN architecture equipped with spatio-temporal interaction modelling and a transformer-based discriminator. The spatio-temporal modelling ability helps to learn the human social interactions better while the transformer-based discriminator design improves temporal sequence modelling. Additionally, SGANv2 utilizes the learned discriminator even at test-time via a collaborative sampling strategy that not only refines the colliding trajectories but also prevents mode collapse, a common phenomenon in GAN training. Through extensive experimentation on multiple real-world and synthetic datasets, we demonstrate the efficacy of SGANv2 to provide socially-compliant multimodal trajectories.

Integrated Decision Making and Motion Control for Autonomous Emergency Avoidance Based on Driving Primitives Transition

Emergency avoidance is an aggressive maneuver with high collision risk. This paper presents an integrated decision making and motion control framework to achieve emergency avoidance in complex driving scenarios. This is realized by combining reasonable decision making based on driving primitives transition and efficient motion control under critical driving conditions. For decision making, the driving primitives including braking, lane changing, and accelerating are generalized to perform a complete emergency avoidance maneuver through the Minimum Safety Spacing analysis, which is numerically executed as a mapping function of the host vehicle's speed and road adhesion coefficient. For motion control, the quintic polynomial-based path planning is formulated as solving a minimum lane changing duration problem. Moreover, the Linear Time-Varying Model Predictive Control combined with the Direct Yaw-moment Control is put forward to realize accurate path tracking control for emergency avoidance. The effectiveness of the proposed scheme is examined under various scenarios based on comprehensive Hardware-in-Loop tests. The results show that the proposed framework can realize timely emergency avoidance with guaranteed vehicle dynamics stability based on proper decision-making and accurate path tracking control.

A new efficient approach for the calculation of cross-sections with application to Yukawa potential

Large-scale and systematic calculations of scattering amplitudes and cross-sections for charged particle collisions are of fundamental importance for understanding the physical properties of materials in different research fields. However, the elaborated theoretical methods for cross-sections are generally restricted to a finite range of impact energies. Here, we present an efficient approach for the calculation of the scattering amplitude and cross-sections ranging from low to high collision energies based on the variable phase method, where the Wentzel–Kramers–Brillouin and Born approximations for scattering phase shifts (SPSs) are incorporated into the numerical algorithm to alleviate the computational cost. For this purpose, quantitative criteria for the validity of these approximations are established based on the properties of the turning points of the potentials. For different scattering potentials, the corresponding planes can be established as a guideline to select the optimal combination for calculating the scattering amplitude and cross-section. The demand for quantum treatment of phase shifts is reduced by one to two orders of magnitude, which strongly benefits the computation of cross-sections for high-energy scattering. It has been found that the quantum treatment for SPSs is necessary near the quantum states involving quantum tunneling and resonance. To testify the validity of the approach, the SPSs and also transport cross-sections are calculated for Yukawa potentials, and good agreements are obtained in comparison with other available high-precision calculations. The proposed numerical approach can be straightforwardly generalized to other scattering potentials and permits one to efficiently calculate the scattering cross-sections for a large energy range.

Dynamics of Dissociative Chemisorption of NH3 on Fe(111) on a Twelve-Dimensional Potential Energy Surface

We constructed a new, accurate twelve-dimensional potential energy surface (PES) for ammonia dissociative chemisorption (DC) on a rigid Fe(111) surface using the fundamental invariant-neural network fitting to a large number of density functional theory data points. The DC dynamics simulations were carried out by the quasi-classical trajectory (QCT) approach. At very low collision energies, the molecules experienced a large number of rebounds due to the deep pre-transition-state adsorption well and finally decomposed around the global transition state. At high collision energies, the bridge site with a loose transition state was the most favorable site for dissociation. The mode-specific dynamics revealed that excitations in symmetric stretch and umbrella modes were most efficient in promoting the dissociation and were more efficacious than the same amount of translational energy. The computed large dissociation probability for NH3/Fe(111) supported the fact that iron is an effective catalyst for ammonia dissociation, which is of practical importance in producing clean hydrogen.

Speed shear rate impact on the properties of OSA-modified potato starch

The reaction between starch granules and octenyl succinic anhydride (OSA) is regularly retarded due to the low breakthrough of large oily OSA droplets into starch granules in an aqueous reaction system. Furthermore, high-speed shearing is widely used in the food industry, demanding high shear, cavitation, and collision force. In this sense, high-speed shearing could reduce the size of OSA droplets and promote a more homogeneous distribution of groups in the starch granule. The aim of this study was to evaluate the impact of OSA potato starch synthesis assisted by high-speed shear on structure (SEM and FTIR), gelatinization, rheology, and emulsifying activity (ES and AS) was investigated. The results showed a gradual increase in DS proportional to the applied speed. Likewise, the OSA starches showed a slight alteration in the shape of the granules (SEM), and FT-IR spectroscopy showed a characteristic absorption of the ester carbonyl groups in the OSA starch at 1724 cm-1. The high-speed shear-treated starches exhibited a significant change in the reduction of the initial gelatinization temperature, although not in the enthalpy. All the gels presented rheology adjusted to the Herschel-Bulkley model with variations in the initial shear stress. Changes in the viscoelastic behavior are proportional to the shear rate detected. High-speed shear treatment did not show a significant effect on emulsion stability (ES) and emulsion activity (EA). Consequently, applying high shear rates allows having OSA starches with different uses.

Revealing the origin of neutrino masses through the Type II Seesaw mechanism at high-energy muon colliders

The future muon collider can play as an ideal machine to search for new physics at high energies. In this work, we study the search potential of the heavy Higgs triplet in the Type II Seesaw mechanism at muon colliders with high collision energy and high luminosity. The latest neutrino oscillation data are taken into account for realizing the leptonic decay modes of the charged Higgs bosons (H±±, H±) in the Type II Seesaw. We show the impact of neutrino mass and mixing parameters on the purely leptonic decays. The pair production of doubly charged Higgs H++H−− is through direct μ+μ− annihilation and vector boson fusion (VBF) processes at muon collider. The associated production H±±H∓ can only be induced by VBF processes. We simulate both the purely leptonic and bosonic signal channels of charged Higgs bosons in Type II Seesaw, together with the Standard Model backgrounds. We show the required luminosity for the discovery of the charged Higgses and the reachable limits on the leptonic decay branching fractions.

Nucleic Acid Amplification by Template-Dominated Click Chemistry for Ultrasensitive DNA/RNA Detection on an Electrochemical Readout Platform.

As an enzyme-free exponential nucleic acid amplification method, the click chemistry-mediated ligation chain reaction (ccLCR) has shown great prospects in the molecular diagnosis. However, the current optics-based ccLCR is challenged by remarkable nonspecific amplification, severely hindering its future application. This study demonstrated that the severe nonspecific amplification was generated probably due to high random collision in the high DNA probe concentration (μM level). To solve this hurdle, a nucleic acid template-dominated ccLCR was constructed using nM-level DNA probes and read on an electrochemical platform (cc-eLCR). Under the optimal conditions, the proposed cc-eLCR detected a low-level nucleic acid target (1 fM) with a single-base resolution. Furthermore, this assay was applied to detect the target of interest in cell extracts with a satisfactory result. The proposed cc-eLCR offers huge possibility for click chemistry-mediated enzyme-free exponential nucleic acid amplification in the application of medical diagnosis and biomedical research.

Free-standing membrane incorporating single-atom catalysts for ultrafast electroreduction of low-concentration nitrate

The release of wastewaters containing relatively low levels of nitrate (NO3-) results in sufficient contamination to induce harmful algal blooms and to elevate drinking water NO3- concentrations to potentially hazardous levels. In particular, the facile triggering of algal blooms by ultra-low concentrations of NO3- necessitates the development of efficient methods for NO3- destruction. However, promising electrochemical methods suffer from weak mass transport under low reactant concentrations, resulting in long treatment times (on the order of hours) for complete NO3- destruction. In this study, we present flow-through electrofiltration via an electrified membrane incorporating nonprecious metal single-atom catalysts for NO3- reduction activity enhancement and selectivity modification, achieving near-complete removal of ultra-low concentration NO3- (10 mg-N L-1) with a residence time of only a few seconds (10 s). By anchoring Cu single atoms supported on N-doped carbon in a carbon nanotube interwoven framework, we fabricate a free-standing carbonaceous membrane featuring high conductivity, permeability, and flexibility. The membrane achieves over 97% NO3- removal with high N2 selectivity of 86% in a single-pass electrofiltration, which is a significant improvement over flow-by operation (30% NO3- removal with 7% N2 selectivity). This high NO3- reduction performance is attributed to the greater adsorption and transport of nitric oxide under high molecular collision frequency coupled with a balanced supply of atomic hydrogen through H2 dissociation during electrofiltration. Overall, our findings provide a paradigm of applying a flow-through electrified membrane incorporating single-atom catalysts to improve the rate and selectivity of NO3- reduction for efficient water purification.

Deep Learning Based Double-Contention Random Access for Massive Machine-Type Communication

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Multiaccess Point Coordination for Next-Gen Wi-Fi Networks Aided by Deep Reinforcement Learning

Wi-Fi in the enterprise - characterized by overlapping Wi-Fi cells - constitutes the design challenge for next-generation networks. Standardization for recently started IEEE 802.11be (Wi-Fi 7) Working Groups has focused on significant medium access control layer changes that emphasize the role of the access point (AP) in radio resource management (RRM) for coordinating channel access due to the high collision probability with the distributed coordination function (DCF), especially in dense overlapping Wi-Fi networks. This paper proposes a novel multi-AP coordination system architecture aided by a centralized AP controller (APC). Meanwhile, a deep reinforcement learning channel access (DLCA) protocol is developed to replace the binary exponential backoff mechanism in DCF to enhance the network throughput by enabling the coordination of APs. First-Order Model-Agnostic Meta-Learning further enhances the network throughput. Subsequently, we also put forward a new greedy algorithm to maintain proportional fairness (PF) among multiple APs. Via the simulation, the performance of DLCA protocol in dense overlapping Wi-Fi networks is verified to have strong stability and outperform baselines such as Shared Transmission Opportunity (SH-TXOP) and Request-to-Send/Clear-to-Send (RTS/CTS) in terms of the network throughput by 10% and 3% as well as the network utility considering proportional fairness by 28.3% and 13.8%, respectively.

Simulations of satellites mock-up fragmentation

High energy in-space collisions may lead to the catastrophic fragmentation of entire spacecraft. Current empirical models employed to predict spacecraft breakup are based on ground experiments and observation of debris cloud generated by collision events. Due to the continuous growth of the number of resident objects orbiting the Earth and the risk they pose to operational satellites, in the last years the interest in collecting new test data on spacecraft collisions has increased, as well as the request to improve current breakup models and develop new ones.In this context the University of Padova performed a set of impact simulations, with a custom fragmentation algorithm, on satellites mock-ups consisting of cubic, cylindrical, and parallelepiped shapes with internal boxes representing on-board components. The considered scenarios include several targets and impactors masses and sizes and different impact geometries (in terms of velocity, impact angle and location). Simulations results consist in the generated fragments characteristic length cumulative distributions. It was observed that all distributions show different sections that can be attributed to different damage modes: the smaller fragments are generated by the spacecraft components fragmentation, the intermediate ones by the detached internal boxes, and the largest ones consisting in intact pieces of the spacecraft separated from the main structure. The limits, extent and slope of these sections depend on the impact conditions, the satellite structure and the impact point; a piecewise analytical model is derived for the simulation data, showing a good accordance with the fragments distribution trends.

Double Proteolysis for N- and O-glycan Analysis of Fc-fusion Protein Etanercept

Introduction. Highly glycosylated proteins are the most abundant class of modern biopharmaceuticals. A majority of such therapeuticals produced by Russian biopharmaceutical companies is biosimilars. The foundation of biosimilar manufacturing is analytical assessment of structure equivalence to an original molecule. Fc-fusions present a challenge due to their structural properties. The only biosimilar of this kind registered in Russia is etanercept – a fusion of tumor necrosis factor receptor α and Fc-fragment of IgG1. Existing approaches widely used in protein analysis do not allow accurate and reliable description of glycoylation of these proteins. Development of new approaches and principles of such analysis is necessary, as the changes in biosimilar’s molecular structure can seriously affect its efficacy and safety.Aim. Development of double proteolysis approaches for glycopeptide mapping of Fc-fusion protein etanercept using Arg-C protease.Materials and methods. Etanercept was subjected to enzymatic hydrolysis using trypsin in combination with Arg-C or Asp-N. The resulting peptides were analyzed using HPLC-MS system Xevo G2-XS QTOF (Waters Corporation, USA). The conformation of glycan structure was performed via analysis of fragment spectra of glycopeptides, acquired with high collision energy mode (MS E ). UNIFI (version 1.8) with biopharmasuetical assessment setting (Waters Corporation, USA) was used to analyze the peptide maps.Results and discussion. It was found that using the combination of trypsin with protease Arg-C leads to reliable results Using the developed approach we successfully determined the majority of O-glycosylation sites and types of O-glycans. It was shown that for an effective O-glycopeptide maping N-deglycosylation stage is required. Most abundant N-glycan structures were identified for each of three N-glycosylation sites (N149, N171, N317). It was determined, that the combination of trypsin with Arg-C allows identification of three-antenna forms despite the presence of O-glycosylation site on the analyzed peptide. General N-glycosylation profile shows comparability of results for both approaches.Conclusion. As a result of this research we developed glycopeptide mapping approaches in which a combination of proteases is used. Using these methods sites of N- and O-glycosilation and glycofoms of etanercept were accurately and reproducibly determined. Developed procedures can be applied to other types of Fc-fusion proteins, making it of broader appeal and benefit to the overall biopharmaceutical industry. These approaches provide comprehensive information useful for structure-function studies and of potential value for product comparability measurements and possibly even future manufacturing control strategies.

Pathophysiological and motor factors associated with collision avoidance behavior in individuals with stroke.

High collision rates and frequency of entering the opening from non-paretic sides are associated with collision in individuals with stroke. To identify factors associated with collision avoidance behavior when individuals with stroke walked through narrow openings. Participants with subacute or chronic stroke walked through a narrow opening and had to avoid colliding with obstacles. Multiple regression analyses were conducted with pathophysiology, motor function, and judgment ability as predictor variables; collision rate and frequency of entering the opening from non-paretic sides were outcome variables. Sixty-one eligible individuals with stroke aged 63±12 years were enrolled. Thirty participants collided twice or more and 37 entered the opening from the non-paretic side. Higher collision occurrence was associated with slower Timed Up and Go tests and left-right sway (odds ratios, 1.2 and 5.6; 95% confidence intervals, 1.1-1.3 and 1.3-28.2; p = .008 and.025, respectively). Entering from non-paretic sides was associated with lesions in the thalamus, left-sided hemiplegia, and Brunnstrom stage 3 or lower (odds ratios, 6.6, 8.7, and 6.7; 95% confidence intervals, 1.3-52.5, 2.5-36.5, and 1.2-57.5; and p = .038,.001, and.048, respectively). Walking ability is associated with avoiding obstacle collision, while pathophysiological characteristics and degree of paralysis are associated with a preference for which side of the body enters an opening first. Interventions to improve walking ability may improve collision avoidance. Avoidance behavior during intervention varies depending on the lesion position.