Acceleration Model Research Articles

The hidden curves of risk: a nonlinear model of cumulative risk and school bullying victimization among adolescents with autism spectrum disorder

BackgroundSchool bullying victimization (SBV) occurs more frequently in students with autism spectrum disorder (ASD) in general education than in special classes, and there is a cumulative risk effect on SBV exposure among young people with ASD reported by their parents and teachers. However, SBV is a personal experience, the predictive patterns of cumulative risk on SBV reported by themselves and its psychological mechanism remain unclear. This study aims to explore the relationship between cumulative risk and SBV based on self-report, and to test whether internalizing problems mediates this relationship among adolescents with ASD placed in regular classes.MethodsThis study used data from the Taiwan Special Needs Education Longitudinal Study (SNELS) in 2011. The analysis included 508 adolescents with ASD who were in regular classes across Taiwan. The primary variables under study were the quality of friendship interactions, teacher-student relationship, school connection, perceived stigma, the impact caused by the disabilities, internalizing problem, and whether the participants had experienced SBV over the past semester, while control variables were adaptability and social-emotional skills. Established risk factors were summed to form a cumulative risk score.ResultsThe cumulative risk was positively associated with SBV. The relationship was characterized by the nonlinear pattern of the quadratic function (negative acceleration model) between cumulative risk and SBV. Internalizing problem played a partial mediating role in the effect of cumulative risk on SBV.ConclusionsIntervention measures to reduce SBV should include the strategies to reduce the number of risks to which adolescents with ASD in regular classes are exposed, comprehensive prevention targeting each risk factor is needed specially when the number of risks is one or two, and more attention needs to be given to their internalizing problem in various ways.

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

Symplectic neural surrogate models for beam dynamics

Development of robust machine-learning (ML) based surrogates for particle accelerators can significantly benefit the modeling, design, optimization, monitoring and control of such accelerators. It is desirable that the surrogate models embed fundamental physical constraints to the interaction and dynamics of the beams, for which an accelerator must be designed to operate upon. We implement and train a class of phase space structure-preserving neural networks — Henon Neural Networks (HenonNets) [1], for nonlinear beam dynamics problems. It is demonstrated that the trained HenonNet model predicts the beam transfer matrix to a reasonably good accuracy while strongly maintaining the symplecticity. To explore such model’s applicability and flexibility for high brightness or intensity beams, we further test it with beam dynamics in the presence of electrostatic and radiative collective effects. Our results indicate that HenonNet may be used as a base ML model for the surrogate of complex beam dynamics, thus opening up a wide range of applications.

Numerical modeling of time dependent Diffusive Shock Acceleration

Motivated by cosmic ray (CR) re-acceleration at a potential Galactic Wind Termination Shock (GWTS), we present a numerical model for time-dependent Diffusive Shock Acceleration (DSA). We use the stochastic differential equation solver (DiffusionSDE) of the cosmic ray propagation framework CRPropa3.2 with two modifications: An importance sampling module is introduced to improve statistics at high energies in order to keep the simulation time short. An adaptive time step is implemented in the DiffusionSDE module. This ensures to efficiently meet constraints on the time and diffusion step, which is crucial to obtain the correct shock spectra. The time evolution of the spectrum at a one-dimensional planar shock is verified against the solution obtained by the grid-based solver VLUGR3 for both energy-independent and energy-dependent diffusion. We show that the injection of pre-accelerated particles can lead to a broken power law spectrum in momentum if the incoming spectrum of CRs is harder than the re-accelerated spectrum. If the injected spectrum is steeper, the shock spectrum dominates at all energies. We finally apply the developed model to the GWTS by considering a spherically symmetric shock, a spiral Galactic magnetic field, and anisotropic diffusion. The time-dependent spectrum at the shock is modeled as a basis for further studies.

Lightweight accident detection model for autonomous fleets based on GPS data

Autonomous Vehicles (AVs) will be the future of automotive including both the Public and the Private Transportation. One of the major concerns of the corresponding research community is the safety of the AVs. Considering this, a lightweight accident detection model for autonomous fleets is presented, utilizing only GPS data. The proposed accident detection model combines well-known statistical and machine learning techniques such as data normalization, PCA transformation, and DBSCAN clustering. In order to validate the proposed methodology simulated data were utilized exploiting well-established techniques, such as Dead-Reckoning, accident speed profiles, and pre-crash acceleration models. The preliminary results highlighted that the proposed methodology managed to achieve its accurate accident detection purpose presenting accuracy higher than 98%.

The Scaling of Vortical Electron Acceleration in Thin-current Magnetic Reconnection and Its Implications in Solar Flares

To investigate how magnetic reconnection (MR) accelerates electrons to a power-law energy spectrum in solar flares, we explore the scaling of a kinetic model proposed by Che & Zank (CZ) and compare it to observations. Focusing on thin current sheet MR particle-in-cell (PIC) simulations, we analyze the impact of domain size on the evolution of the electron Kelvin–Helmholtz instability (EKHI). We find that the duration of the growth stage of the EKHI ( tG∼Ωe−1 ) is short and remains nearly unchanged because the electron gyrofrequency Ω e is independent of domain size. The quasi-steady stage of the EKHI (t MR) dominates the electron acceleration process and scales linearly with the size of the simulations as L/v A0, where v A0 is the Alfvén speed. We use the analytical results obtained by CZ to calculate the continuous temporal evolution of the electron energy spectra from PIC simulations and linearly scale them to solar flare observational scales. For the first time, an electron acceleration model predicts the sharp two-stage transition observed in typical soft–hard–harder electron energy spectra, implying that the electron acceleration model must be efficient with an acceleration timescale that is a small fraction of the duration of solar flares. Our results suggest that we can use PIC MR simulations to investigate the observational electron energy spectral evolution of solar flares if the ratio t MR/t G is sufficiently small, i.e., ≲10%.

Refined Modeling of Geoeffective Fast Halo CMEs During Solar Cycle 24

AbstractThe propagation of geoeffective fast halo coronal mass ejections (CMEs) from solar cycle 24 has been investigated using the European Heliospheric Forecasting Information Asset (EUHFORIA), ENLIL, Drag‐Based Model (DBM) and Effective Acceleration Model (EAM) models. For an objective comparison, a unified set of a small sample of CME events with similar characteristics has been selected. The same CME kinematic parameters have been used as input in the propagation models to compare their predicted arrival times and the speed of the interplanetary (IP) shocks associated with the CMEs. The performance assessment has been based on the application of an identical set of metrics. First, the modeling of the events has been done with default input concerning the background solar wind, as would be used in operations. The obtained CME arrival forecast deviates from the observations at L1, with a general underestimation of the arrival time and overestimation of the impact speed (mean absolute error [MAE]: 9.8 ± 1.8–14.6 ± 2.3 hr and 178 ± 22–376 ± 54 km/s). To address this discrepancy, we refine the models by simple changes of the density ratio (dcld) between the CME and IP space in the numerical, and the IP drag (γ) in the analytical models. This approach resulted in a reduced MAE in the forecast for the arrival time of 8.6 ± 2.2–13.5 ± 2.2 hr and the impact speed of 51 ± 6–243 ± 45 km/s. In addition, we performed multi‐CME runs to simulate potential interactions. This leads, to even larger uncertainties in the forecast. Based on this study we suggest simple adjustments in the operational settings for improving the forecast of fast halo CMEs.

Acceleration as an Ecosystem’s Unique Mechanism: A Systematic Literature Review and Ecosystem Acceleration Model

Acceleration as an Ecosystem’s Unique Mechanism: A Systematic Literature Review and Ecosystem Acceleration Model

Построение и калибровка DSGE модели для российской экономики с использованием импульсных откликов векторной авторегрессии

The paper proposes a two‐sector macroeconomic model of the Russian economy based on the standard assumptions of New Keynesian DSGE models used to model household consumption, price and wage rigidities, and endogenous capital utilization. We consider two options for describing the investment process: the traditional approach with the investment adjustment costs and the approach using the investment accelerator model. The model parameters are calibrated based on minimizing the distance between the theoretical and “empirical” impulse response functions to the terms of trade shock derived from estimating simple ARX models with terms of trade as an exogenous variable. The constructed model quite accurately reproduces the influence of the terms of trade on the Russian economy for both investment modeling options. Based on the calibrated model, the authors examined the impact of a monetary policy shock on macroeconomic indicators and constructed a historical decomposition of the dynamics of macroeconomic indicators by an extended set of structural shocks to economic variables.

Constraints on Solar Wind Density and Velocity Based on Coronal Tomography and Parker Solar Probe Measurements

Previous work has established an empirical relationship between densities gained from coronal rotational tomography near the ecliptic plane with solar wind outflow speeds at heliocentric distance r 0 = 8R ⊙. This work aims to include solar wind acceleration, and thus velocity profiles out to 1 au. Inner boundary velocities are given as a function of normalized tomographic densities, ρ N , as V0=75∗e−5.2*ρN+108 , and typically range from 100 to 180 km s−1. The subsequent acceleration is defined as V(r)=V01+αIP1−e−r−r0/rH , with α IP ranging between 1.75 and 2.7, and r H between 50 and 35 R ⊙ dependent on V 0. These acceleration profiles approximate the distribution of in situ measurements by Parker Solar Probe (PSP) and other measurements at 1 au. Between 2018 November and 2021 September these constraints are applied using the HUXt model and give good agreement with in situ observations at PSP, with a ∼6% improvement compared with using a simpler constant acceleration model previously considered. Given the known tomographical densities at 8 R ⊙, we extrapolate density to 1 au using the model velocities and assuming mass flux conservation. Extrapolated densities agree well with OMNI measurements. Thus coronagraph-based estimates of densities define the ambient solar wind outflow speed, acceleration, and density from 8 R ⊙ to at least 1 au. This sets a constraint on more advanced models, and a framework for forecasting that provides a valid alternative to the use of velocities derived from magnetic field extrapolations.

Animal spirits, bankruptcies, and monetary policy effectiveness in a hybrid macroeconomic agent-based financial accelerator model

This paper develops a hybrid model with an agent-based financial accelerator framework embedded in a standard new Keynesian economy. It explores the interactions between the financial accelerator and the credit market, focusing on the effects of bankruptcy. The paper replicates credit-market relationships, modeling various credit crunch scenarios. It uncovers endogenous fluctuations and “animal spirits” in entrepreneurs’ expectations, driving investment and production decisions. Notably, higher pro-cyclical leverage can have destabilizing effects in the presence of small shocks, increasing entrepreneurs’ bankruptcies. The results suggest that monetary policy’s effectiveness in stabilizing fluctuations depends on factors such as heterogeneity, bounded rationality, and heuristic updating mechanisms. Moderate monetary policies perform better in terms of economic growth with moderate-to-low volatility, while aggressive policies on inflation assist bounded rational agents in reducing errors in investment decisions and default rates, fostering a more stable macroeconomic environment. Increasing forecasting options introduces diversification among entrepreneurs, reducing volatility and stabilizing investments. More options mitigate investment fluctuations, acting as a counterbalance to prevailing market sentiments. Over time, individuals weakly adhering to trends or adopting contrarian approaches come to dominate the population of entrepreneurs, enhancing the overall stability of investment decisions.

Weight-Drop Method for Inducing Closed Head Diffuse Traumatic Brain Injury.

Traumatic brain injury (TBI) is one of the foremost causes of disability and death globally. Prerequisites for successful therapy of disabilities associated with TBI involved improved knowledge of the neurobiology of TBI, measurement of quantitative changes in recovery dynamics brought about by therapy, and the translation of quantitative methodologies and techniques that were successful in tracking recovery in preclinical models to human TBI. Frequently used animal models of TBI in research and development include controlled cortical impact, fluid percussion injury, blast injury, penetrating blast brain injury, and weight-drop impact acceleration models. Preclinical models of TBI benefit from controlled injury settings and the best prospects for biometric quantification of injury and therapy-induced gradual recovery from disabilities. Impact acceleration closed head TBI paradigm causes diffuse TBI (DTBI) without substantial focal brain lesions in rats. DTBI is linked to a significant rate of death, morbidity, and long-term disability. DTBI is difficult to diagnose at the time of hospitalization with imaging techniques making it challenging to take prompt therapeutic action. The weight-drop method without craniotomy is an impact acceleration closed head DTBI model that is used to induce mild/moderate diffuse brain injuries in rodents. Additionally, we have characterized neuropathological and neurobehavioral outcomes of the weight-drop model without craniotomy for inducing closed head DTBI of graded severity with a range of mass of weights (50-450gm). This chapter also discusses techniques and protocols for measuring numerous functional disabilities and pathological changes in the brain brought on by DTBI.

Acceleration as an ecosystem's unique mechanism: a systematic literature review and ecosystem acceleration model

Acceleration as an ecosystem's unique mechanism: a systematic literature review and ecosystem acceleration model

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

Single‐ and multi‐GPU computing on NVIDIA‐ and AMD‐based server platforms for solidification modeling application

SummaryThis work explores the performance of single‐ and multi‐GPU computing on state‐of‐the‐art NVIDIA‐ and AMD‐based server‐class hardware using various programming interfaces to accelerate a real‐world scientific application for solidification modeling based on the phase‐field method. The main computations of this memory‐bound application correspond to 20 stencils computed across grid nodes. We investigate the application's scalability for two basic schemes of organizing computation: without and with hiding data transfers behind computation, combined with using either peer‐to‐peer inter‐GPU data transfers through NVIDIA NVLink and AMD Infinity interconnects or communication over the PCIe and main memory. Among the studied programming interfaces is CUDA, HIP, and OpenMP Accelerator Model. While the first two are designed to write the codes for a specific hardware platform, OpenMP enables code portability between NVIDIA and AMD GPUs. The resulting performance is experimentally assessed on computing platforms containing NVIDIA V100 (up to 8 GPUs) and A100 (one GPU), as well as AMD MI210 (one device) and MI250 (up to 8 logical GPUs).

Criticism of the justification of history acceleration with the help of synergetics and the concept of “Big History”

There are a large number of different models of history acceleration. This article discusses the models proposed by S. P. Kapitsa, S. V. Tsirel and A. D. Panov. They are based on the prerequisites of a synergetic methodology, the concepts of Big History and singularity, as well as the interpretation of the acceleration of history as a consistent reduction in the duration of significant historical epochs. The purpose of the paper is to show the unconvincing nature of such an approach as a variant of substantiating the conception of history acceleration.

Interaction of Organogermanium Compounds with Saccharides in Aqueous Solutions: Promotion of Aldose-to-ketose Isomerization and Its Molecular Mechanism.

This review discusses sugar isomerization with organogermanium compounds. Organogermanium compounds markedly increase the aldose-ketose (glucose-fructose or lactose-lactulose) isomerization ratio, double the initial reaction rate, and significantly reduce the base-catalyzed degradation of sugars. 1H-nuclear magnetic resonance analysis reveals that the affinity of organogermanium compounds with a 3-(trihydroxygermyl)propanoic acid (THGP) structure toward ketoses is 20-40 times stronger than that toward aldoses; thus, such organogermanium compounds form complexes more readily with ketoses than with aldoses. Stable ketose complexes, which contain multiple cis-diol structures and high fractions of furanose structures, suppress the reverse ketose-aldose reaction, thereby shifting the equilibrium toward the ketose side. These complexes also protect sugar molecules from alkaline degradation owing to the repulsion between anionic charges. The increased rate of the initial reaction in the alkaline isomerization process results from stabilizing the transition state by forming a complex between THGP and a cis-enediol intermediate. The cyclic pentacoordinate or hexacoordinate THGP structures give rise to a conjugated system of germanium orbitals, which is extended through dπ-pπ interactions, thereby improving the stability of the complex. Based on these results, we have developed a bench-scale lactulose syrup manufacturing plant incorporating a system to separate, recover, and reuse organogermanium poly-trans-[(2-carboxyethyl)germasesquioxane]. This manufacturing plant can be used as a model of an alkaline isomerization accelerator for continuous industrial production.

The Potential Role of Manufacturers' Orders in the Linear-Quadratic Flexible Accelerator Model

The linear-quadratic flexible accelerator model has been a staple for empirical analysis within the inventories literature. A key assumption of the model is that sales are a proxy for demand, thus inventories are generated in instances where production does not equilibrate with demand. We seek to improve upon the benchmark linear-quadratic model by introducing firm orders, thus allowing for differentiation between realized and expected demand. Estimation results suggest that the omission of orders heavily biases the coefficient sign and magnitude associated with sales. Furthermore, the estimated adjustment speed of orders is both larger in magnitude and statistical significance than for sales. The disparity in the rates of adjustment between expected and realized demand provides a new contribution towards understanding the adjustment speed puzzles pervasive in the literature. Finally, the addition of orders provides stronger evidence of a cointegrating relationship in a trivariate system of equations versus a standard bivariate system containing strictly inventories and sales. However, exogenous order shocks do not seem to meaningfully impact inventory investment, nor sales. Furthermore, forecast error variance for sales, and inventories are not well explained by orders. These results suggest that orders are important in explaining the underlying data generating processes for sales, and inventories, as well as the long-run relationship between inventory, and sales, but offer little in forecasting potential.

Match Exposure, Consecutive Match Number, and Recovery Days Affect Match Running During International Women's Soccer Tournaments.

Brown, G, Massard, T, Wignell, T, McCall, A, and Duffield, R. Match exposure, consecutive match number, and recovery days affect match running during international women's soccer tournaments. J Strength Cond Res 38(3): 577-583, 2024-To determine the influence of tournament-specific factors (match exposure, consecutive matches, and days between matches) on match running in women's international soccer players (footballers), 28 players from 1 national team were observed over 4 international tournaments using 10 Hz global positioning system units. Linear mixed models with a forward selection approach were used to analyze the collective effect of tournament-specific factors of match exposure (playing time), consecutive matches (count), days between matches (count) and time on pitch on relative (min -1 ) match running metrics (total distance, high-speed running, very high-speed running, accelerations, and decelerations), and the change in these metrics between halves. The significance level was set at p < 0.05. Days between matches were included in the final models for total distance ( p < 0.0001), high-speed running ( p = 0.002), very high-speed running ( p = 0.025), and change in accelerations ( p < 0.0001) and decelerations ( p = 0.002). Prior match exposure was included in the models for total distance ( p = 0.021), change in total distance ( p < 0.0001), and change in high-speed running ( p = 0.0008). Consecutive match number was included in the model for deceleration ( p = 0.030), change in very high-speed running ( p = 0.016), change in acceleration ( p < 0.0001), and change in deceleration ( p < 0.0001). Time on pitch was included in the final total distance ( p = 0.044), high-speed running ( p < 0.0001), acceleration ( p < 0.0001), and deceleration ( p = 0.0003) models. Overall, reduced match running throughout tournaments was associated with increased time on pitch and ≤3 days between matches. Increasing match exposure and more time between matches minimizes reductions in match running between halves. Practitioners can use these variables to inform player monitoring during international soccer tournaments and on return to clubs.

A Statistical Model of CME Acceleration

A Statistical Model of CME Acceleration