Autocorrelation Function Analysis Research Articles

Self-Similar Mode of Ice Surface Softening During Friction

Softening of ice surface under friction is explored in terms of the rheological model for viscoelastic matter approximation. The non-linear relaxation of strain and fractional feedbacks are allowed. Additive non-correlated noise associated with shear strain, stress as well as with temperature of ice surface layer, is introduced, and a phase diagram is built where the noise intensities of the stress and temperature define the domains of crystalline ice, softened ice, and two types of their mixture (stick-slip friction). Conditions are revealed under which crystalline ice and stick-slip friction proceed in the self-similar mode. Corresponding strain power-law distribution is provided by temperature fluctuations that are much larger than noise intensities of strain and stress. This behavior is fixed by homogenous probability density in which characteristic strain scale is absent. Since the power-type distribution is observed at minor strains, it meets self-similar crystalline ice surface. Constructed friction force time series are investigated for all rubbing regimes using fast Fourier transformation and autocorrelation function analysis. It is revealed that these series represent “pink”-colored noise and weak correlations are realized in the system. The conclusion is drawn that the results of tribological experiment can be forecasted.

X-Ray Flux and Spectral Variability of Six TeV Blazars with NuSTAR

Abstract We report the first results of timing and spectral studies of Nuclear Spectroscopic Telescope Array observations of six TeV-emitting high-frequency peaked blazars: 1ES 0347-121, 1ES 0414+009, RGB J0710+591, 1ES 1101-232, 1ES 1218+304, and H 2356-309. Two out of these six TeV blazars, 1ES 1101−232 and 1ES 1218+304, showed strong evidence of intraday variations in the 3–79 keV energy range during those observations. We also found a hint of an intraday variability timescale of 23.5 ks in the light curve of 1ES 1218+304 using an autocorrelation function analysis. We obtained a magnetic field B ∼ 0.03 G, electron Lorentz factor γ ∼ 2.16 × 106, and emission region size R ∼ 1.19 × 1016 cm for 1ES 1218+304 using that variability timescale. The other blazars’ light curves do not show any variability timescales shorter than their observation lengths; however, we note that the data were both noisier and sparser for them. We also investigated the spectral shape of these TeV blazars and found that the spectrum of 1ES 0414+009 is well described by a single power law with a photon index Γ ∼ 2.77. The spectra of the other five HBLs are somewhat better represented by log-parabola models with local photon indices (at 10 keV) α ∼ 2.23 − 2.67 and curvature parameters β ∼ 0.27 − 0.43.

Thermal conductivity of carbon nanotube superlattices: Comparative study with defective carbon nanotubes**Project supported by the National Natural Science Foundation of China (Grant Nos. 11404278 and 11275163) and the Science Foundation of Hunan Province, China (Grant No. 2016JJ2131).

We use molecular dynamics simulation to calculate the thermal conductivities of (5, 5) carbon nanotube superlattices (CNTSLs) and defective carbon nanotubes (DCNTs), where CNTSLs and DCNTs have the same size. It is found that the thermal conductivity of DCNT is lower than that of CNTSL at the same concentration of Stone–Wales (SW) defects. We perform the analysis of heat current autocorrelation functions and observe the phonon coherent resonance in CNTSLs, but do not observe the same effect in DCNTs. The phonon vibrational eigen-mode analysis reveals that all modes of phonons are strongly localized by SW defects. The degree of localization of CNTSLs is lower than that of DCNTs, because the phonon coherent resonance results in the phonon tunneling effect in the longitudinal phonon mode. The results are helpful in understanding and tuning the thermal conductivity of carbon nanotubes by defect engineering.

Seasonality and multiannual variability of floods: case study from Eastern Poland

Nowadays, under increasing climate change effects on the environment, we can observe increasing number of extreme phenomena, including meteorological and hydrological ones. One of such phenomena are floods. The objective of this article is the assessment of basic flood characteristics seasonality in the annual distribution. Analysis were performed based on time series of daily flow values recorded in the years 1951–2014 in three gauging stations located on rivers in Easter Poland, in upper Wieprz catchment. Floods were defined according to TLM algorithm and were assumed to be all cases of flow occurrence exceeding 10% read from FDC (flow duration curve) (Q10). Seasonality was analysed using Markham’s Seasonality Index and Period of Seasonal Concentration, analysis of autocorrelation function (ACF) as well as proposed by the author Seasonal Winter Floods Index. The distribution of floods during year indicates one flood season in year which occurs in the spring.

A Novel Acoustic Liquid Level Determination Method for Coal Seam Gas Wells Based on Autocorrelation Analysis

In coal seam gas (CSG) wells, water is periodically removed from the wellbore in order to keep the bottom-hole flowing pressure at low levels, facilitating the desorption of methane gas from the coal bed. In order to calculate gas flow rate and further optimize well performance, it is necessary to accurately monitor the liquid level in real-time. This paper presents a novel method based on autocorrelation function (ACF) analysis for determining the liquid level in CSG wells under intense noise conditions. The method involves the calculation of the acoustic travel time in the annulus and processing the autocorrelation signal in order to extract the weak echo under high background noise. In contrast to previous works, the non-linear dependence of the acoustic velocity on temperature and pressure is taken into account. To locate the liquid level of a coal seam gas well the travel time is computed iteratively with the non-linear velocity model. Afterwards, the proposed method is validated using experimental laboratory investigations that have been developed for liquid level detection under two scenarios, representing the combination of low pressure, weak signal, and intense noise generated by gas flowing and leakage. By adopting an evaluation indicator called Crest Factor, the results have shown the superiority of the ACF-based method compared to Fourier filtering (FFT). In the two scenarios, the maximal measurement error from the proposed method was 0.34% and 0.50%, respectively. The latent periodic characteristic of the reflected signal can be extracted by the ACF-based method even when the noise is larger than 1.42 Pa, which is impossible for FFT-based de-noising. A case study focused on a specific CSG well is presented to illustrate the feasibility of the proposed approach, and also to demonstrate that signal processing with autocorrelation analysis can improve the sensitivity of the detection system.

A better understanding of long-range temporal dependence of traffic flow time series

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting

The recent trend for data-driven streamflow forecasting is to hybridize artificial intelligence with decomposition pre-processing. In this paper, a decomposition-based data-driven model called FT-SVR that exploits both Fourier transform (FT) and support vector regression (SVR) techniques is proposed for monthly reservoir inflow forecasting and the Three Gorges Dam (TGD) located on the Yangtze River in China is taken as the case for study. As the inflow time series contains oscillations of disparate scales, FT-SVR uses FT to appropriately decompose the series into multiple decomposed components, with each component comprising of neighboring frequencies and having a clear physical meaning. SVR is employed to develop an independent forecasting model for each decomposed component. The development of each SVR model involves data normalization, input selection based on autocorrelation function and partial autocorrelation function analysis, and parameter calibration by a metaheuristic. FT-SVR is compared with three other models which are the same with FT-SVR except that one uses ensemble empirical mode decomposition, one uses singular spectrum analysis for decomposition, and the other one performs no decomposition. Experimental results demonstrate that FT-SVR is able to give almost perfect monthly inflow forecasting for the TGD and significantly outperforms the three other models, in terms of evaluation criteria including root mean squared error, correlation coefficient, mean average percentage error, Nash-Sutcliffe efficiency coefficient, and relative error of maximum/minimum monthly inflow.

Developing a dengue forecast model using machine learning: A case study in China.

BackgroundIn China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue.Methodology/Principal findingsWeekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China.Conclusion and significanceThe proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

Geomagnetic storm under laboratory conditions: randomized experiment.

The influence of the previously recorded geomagnetic storm (GS) on human cardiovascular system and microcirculation has been studied under laboratory conditions. Healthy volunteers in lying position were exposed under two artificially created conditions: quiet (Q) and storm (S). The Q regime playbacks a noise-free magnetic field (MF) which is closed to the natural geomagnetic conditions on Moscow's latitude. The S regime playbacks the initially recorded 6-h geomagnetic storm which is repeated four times sequentially. The cardiovascular response to the GS impact was assessed by measuring capillary blood velocity (CBV) and blood pressure (BP) and by the analysis of the 24-h ECG recording. A storm-to-quiet ratio for the cardio intervals (CI) and the heart rate variability (HRV) was introduced in order to reveal the average over group significant differences of HRV. An individual sensitivity to the GS was estimated using the autocorrelation function analysis of the high-frequency (HF) part of the CI spectrum. The autocorrelation analysis allowed for detection a group of subjects of study which autocorrelation functions (ACF) react differently in the Q and S regimes of exposure.

Coupled electron-nuclear quantum dynamics through and around a conical intersection.

In solving the time-dependent Schrödinger equation for a coupled electron-nuclear system, we study the motion of wave packets in a model which exhibits a conical intersection (CoIn) of adiabatic potential energy surfaces. Three different situations are studied. In the first case, an efficient non-adiabatic transition takes place while the wave packet passes the region of the CoIn. It is demonstrated that during these times, the nuclear probability density retains its Gaussian shape and the electronic density remains approximately constant. Second, dynamics are regarded where non-adiabatic transitions do not take place, and the nuclear dynamics follows a circle around the location of the CoIn. During this motion, the electronic density is shown to rotate. The comparison with the Born-Oppenheimer nuclear dynamics reveals the geometrical phase being associated with the circular motion. This phase is clearly revealed by an analysis of time-dependent autocorrelation functions and spectra obtained from the numerically exact and the Born-Oppenheimer calculation. The intermediate situation with a small non-adiabatic transition probability is characterized by wave-packet splitting into several fractions.

X-Ray Intraday Variability of Five TeV Blazars with NuSTAR

Abstract We have examined 40 Nuclear Spectroscopic Telescope Array (NuSTAR) light curves (LCs) of five TeV emitting high synchrotron peaked blazars: 1ES 0229+200, Mrk 421, Mrk 501, 1ES 1959+650, and PKS 2155−304. Four of the blazars showed intraday variability in the NuSTAR energy range of 3–79 keV. Using an autocorrelation function analysis we searched for intraday variability timescales in these LCs and found indications of several between 2.5 and 32.8 ks in eight LCs of Mrk 421, a timescale around 8.0 ks for one LC of Mrk 501, and timescales of 29.6 and 57.4 ks in two LCs of PKS 2155-304. The other two blazars’ LCs do not show any evidence for intraday variability timescales shorter than the lengths of those observations; however, the data were both sparser and noisier for them. We found positive correlations with zero lag between soft (3–10 keV) and hard (10–79 keV) bands for most of the LCs, indicating that their emissions originate from the same electron population. We examined spectral variability using a hardness ratio analysis and noticed a general “harder-when-brighter” behavior. The 22 LCs of Mrk 421 observed between 2012 July and 2013 April show that this source was in a quiescent state for an extended period of time and then underwent an unprecedented double-peaked outburst while monitored on a daily basis during 2013 April 10–16. We briefly discuss models capable of explaining these blazar emissions.

Strain and anisotropy effects studied in InAs/GaAs(2 2 1) quantum dashes by Raman spectroscopy

Quantum dashes were synthesized in the molecular beam epitaxial growth of InAs on GaAs(221). By changing the arsenic pressure it was possible to obtain highly ordered one-dimensional InAs arrays as demonstrated by autocorrelation function analysis. Polarized Raman spectroscopy was utilized in order to characterize the samples and to estimate the stress at the InAs/GaAs interface as well as the surface anisotropy imposed by the quasi one-dimensional character of the quantum dashes. The most ordered surface, showed the lowest correlation length, and for this sample the Raman spectra exhibits small shift of the GaAs resonance modes indicating likewise small GaAs tensile strain.

Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine

Precise prediction of wind power can not only conduct wind turbine's operation, but also reduce the impact on power systems when wind energy is injected into the grid. A hybrid autoregressive fractionally integrated moving average and least square support vector machine model is proposed to forecast short-term wind power. The proposed hybrid model takes advantage of the respective superiority of autoregressive fractionally integrated moving average and least square support vector machine. First, the autocorrelation function analysis is used to detect the long memory characteristics of wind power series, and the autoregressive fractionally integrated moving average model is applied to forecast linear component of wind power series. Then the least square support vector machine model is established to forecast nonlinear component of wind power series by making use of wind speed, wind direction and residual error series of the autoregressive fractionally integrated moving average model. Finally, the prediction of wind power is obtained by integrating the prediction results of autoregressive fractionally integrated moving average and least square support vector machine. Compared with other models, the results of two examples demonstrate that the proposed hybrid model has higher accuracy of wind power prediction in terms of three performance indicators.

Home advantage in elite soccer matches. A transient effect?

The aim of the current study was to identify how the performances of home and away teams evolve during the first half of matches and to investigate whether or not the home advantage effect disappears or weakens as the game goes on. The sample consisted of 90 matches during the season 2015/2016 from the English FA Premier League (n = 19), French Ligue 1 (n = 21), Spanish La Liga (n = 18), Italian Serie A (n = 19) and German Bundesliga (n = 13) in which the first half ended with a score of 0–0. Data were examined using the autocorrelation function and linear regression analysis. Ball possession (p < .01), successful passes (p < .05), short passes (p < .01) and touches (p < .01) achieved their greatest values in the 0–5 min period compared with other match periods. However, as the first half went on, the home teams exhibited lower performances compared with those shown at the beginning of the match. This was particularly evident from the 16–20 min period until the end of the first half. This transient effect was enhanced or attenuated, depending on the quality of the opponent (p < .01). The better the opponent the lower the home teams’ performances at the beginning of the match. These variables can be used to develop a model to simulate team performance of home and away teams during the first half of a match.

Density of states and dynamical crossover in a dense fluid revealed by exponential mode analysis of the velocity autocorrelation function.

Extending a preceding study of the velocity autocorrelation function (VAF) in a simulated Lennard-Jones fluid [Phys. Rev. E 92, 042166 (2015)PLEEE81539-375510.1103/PhysRevE.92.042166] to cover higher-density and lower-temperature states, we show that the recently demonstrated multiexponential expansion method allows for a full account and understanding of the basic dynamical processes encompassed by a fundamental quantity as the VAF. In particular, besides obtaining evidence of a persisting long-time tail, we assign specific and unambiguous physical meanings to groups of exponential modes related to the longitudinal and transverse collective dynamics, respectively. We have made this possible by consistently introducing the interpretation of the VAF frequency spectrum as a global density of states in fluids, generalizing a solid-state concept, and by giving to specific spectral components, obtained through the VAF exponential expansion, the corresponding meaning of partial densities of states relative to specific dynamical processes. The clear identification of a high-frequency oscillation of the VAF with the near-top excitation frequency in the dispersion curve of acoustic waves is a neat example of the power of the method. As for the transverse mode contribution, its analysis turns out to be particularly important, because the multiexponential expansion reveals a transition marking the onset of propagating excitations when the density is increased beyond a threshold value. While this finding agrees with the recent literature debating the issue of dynamical crossover boundaries, such as the one identified with the Frenkel line, we can add detailed information on the modes involved in this specific process in the domains of both time and frequency. This will help obtain a still missing full account of transverse dynamics, in both its nonpropagating and propagating aspects which are linked through dynamical transitions depending on both the thermodynamic states and the excitation wave vectors.

Tracing Dynamics, Self-Diffusion, and Nanoscale Structural Heterogeneity of Pure and Binary Mixtures of Ionic Liquid 1-Hexyl-2,3-dimethylimidazolium Bis(fluorosulfonyl)imide with Acetonitrile: Insights from Molecular Dynamics Simulations.

All-atom molecular dynamics (MD) simulations of the ionic liquid (IL) 1-hexyl-2,3-dimethylimidazolium bis(fluorosulfonyl)imide ([C6mmim][FSI]) and its binary mixtures with acetonitrile (ACN) have been reported for the first time. The presence of ACN as a cosolvent, similar to the effect of increasing temperature, causes enhancements in the ion translational motion and fluidity of the IL, leading to significant improvement of ionic conductivity and self-diffusion, which is well explained by a microscopic structural analysis. In neat IL and a concentrated IL mixture, self-diffusion of the cation is higher than that of the corresponding anion; however, further addition of ACN into the diluted mixtures with IL molar fractions (xIL's) below 0.50 results in more weakened interactions among the nearest ACN-anion neighbors compared to those among the ACN-cation neighbors so that the number of isolated anions is more than that of isolated cations under this condition, and the anions diffuse faster than the cations, as expected on the basis of their relative sizes. The velocity autocorrelation function analysis indicates an inverse relation between xIL and the mean collision time of each species. Additionally, at a fixed xIL, both the mean collision time and velocity randomization time of ACN are shorter than those of the ions. Gradual addition of ACN changes the morphology of nanosegregated domains and tends to disrupt ionic clusters (i.e., it scatters and decomposes both the polar and nonpolar domains) compared with pure IL, whereas both the radial and spatial distribution functions show the stabilization role of ACN in the close-contact ion-pair association. On the other hand, increasing ACN causes weakening of the structural correlations of the cation-cation and anion-anion neighbors in the solutions. ACN molecules appeared as a bridge, with balanced affinities between the polar and nonpolar domains, and no indication was observed for aggregation of ACN molecules in the studied mixtures that can rationalize good miscibility with imidazolium-based ILs.

Characteristic quantitative evaluation and stochastic modeling of surface topography for zirconia alumina abrasive belt

Study of abrasive belt topography is the basis to understand wear evolution during belt grinding. Considering its importance and research gap, this paper proposed a quantitative characteristic evaluation method for abrasive belt surface, in addition, also provided a stochastic simulation approach for zirconia alumina abrasive belt based on measured assessment results. In quantitative evaluation, power spectrum density analysis, and autocorrelation function analysis were operated to determine the macroscopic texture feature through frequency structure, cutoff frequency, autocorrelation length, and texture vertical ratio. Besides, a set of parameters was defined in character statistics analysis to describe abrasive belt surface more specifically, including area ratio, summit density, cutting edge protrusion height, interspacing of grains, tip radius, and cone angle. The measured results illustrate that zirconia alumina abrasive belt topography is kind of isotropy surface with nearly constant area ratio and is constituted by three parts of waviness with different wavelength. The mathematical probability distribution models of protrusion height, inter-grain spacing, and cone angle accord with Gaussian distribution, while tip radius obeys Gamma distribution. Based on above conclusions, a stochastic simulation using a novel shuttle-shaped grain model was developed. The simulation is based on random spatial movement of grains with certain distribution regularity. The similarity of characteristic parameters between the experimental and numerical results, including summit density, cutting edge protrusion height, and inter-grain spacing, has successfully validated the stochastic model. This quantitative description and topography modeling provide a valuable foundation for investigating abrasive belt wear mechanisms and its effect on workpiece surface roughness.

Generation and implementation of IRNSS Standard Positioning Signal

The two sorts of services given by the Indian Regional Navigational Satellite System (IRNSS) satellites are Standard Positioning Service (SPS) and Restricted Service (RS). Both services will be given at two frequencies of L5 (1164.5 MHz) and S (2472.5 MHz) band. The code sequences utilized as a part of SPS are Pseudo Ranging Noise (PRN) codes. They utilize gold codes for navigational data transmission in SPS downlink. PRN sequence code is the secondary code and the gold code is the primary code. The greater part of the global positioning system works on Code Division Multiple Access (CDMA), in which Pseudo Random Code (PRN) sequences are required for the systems. In this paper a study is made on the generation and properties of the PRN codes from the navigational system viewpoint. This paper additionally shows the design and implementation of PRN code on Spartan-II FPGA hardware. The generated SPS PRN code results are approved from hardware with the simulation results and examination of the properties of the PRN codes positively acquired. This paper likewise exhibits the execution analysis and simulation of Auto-Correlation Function (ACF) and Cross-Correlation Function (CCF) properties for PRN sequence. The simulations of SPS PRN codes were completed utilizing the Xilinx ISE test system and MATLAB apparatus. The simulated test outcomes are within the theoretical limits.

Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach

The present study focuses on the application of stochastic modeling technique in analyzing the future trends of aerosol optical properties. For this, the Box–Jenkins ARIMA (Autoregressive Integrated Moving Average) model has been used for simulating the monthly average Aerosol Optical Depth (AOD550 nm) retrieved from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) over New Delhi, the urban capital of India. The satellite dataset has been collected for a period of ten years from 2004 to 2014. The analysis of autocorrelation function indicates existence of seasonality in the AOD time series. Several seasonal ARIMA models have been generated and their validation has been verified by assessing various estimation parameters, using the Statistical Package for the Social Sciences (SPSS, version 20). After rigorous evaluation of the selected models, the ARIMA (1,0,0)x(0,1,2)12 is identified as the best fit model w.r.t. measures of goodness-of-fit like Stationary R-square (0.530), R-square (0.674), Root Mean Squared Error (0.128); Mean Absolute Error (0.095); Mean Absolute Percentage Error (16.942); and normalized Bayesian Information Criteria (−3.941). The selected models have been further used to forecast AOD values for the year 2014 at 95% level of confidence. However, the ARIMA (1,0,0)x(2,1,1)12 model is found to have minimum forecasting error, calculated as Mean Percentage Error (0.220). As the difference in BIC of both the models is minimal (0.046), so both the models have been considered as best fit models and utilized for prediction of AOD. Satisfactory results have been obtained using the selected ARIMA models, suggesting that a simplistic modeling technique for determining the future values of AOD is feasible.

Ab initio molecular dynamics study for C–Br dissociation within BrH2C–C≡C(ads) adsorbed on an Ag(111) surface: a short-time Fourier transform approach

The reaction dynamics for C–Br dissociation within BrH2C–C≡CH(ads) adsorbed on an Ag(111) surface has been investigated by combining density functional theory-based molecular dynamics simulations with short-time Fourier transform (STFT) analysis of the dipole moment autocorrelation function. Two possible reaction pathways for C–Br scission within BrH2C–C≡CH(ads) have been proposed on the basis of different initial structural models. Firstly, the initial perpendicular orientation of adsorbed BrH2C–C≡CH(ads) with a stronger C–Br bond will undergo dynamic rotation leading to the final parallel orientation of BrH2C–C≡CH(ads) to cause the C–Br scission, namely, an indirect dissociation pathway. Secondly, the initial parallel orientation of adsorbed BrH2C–C≡C(ads) with a weaker C–Br bond will directly cause the C–Br scission within BrH2C–C≡CH(ads), namely, a direct dissociation pathway. To further investigate the evolution of different vibrational modes of BrH2C–C≡CH(ads) along these two reaction pathways, the STFT analysis is performed to illustrate that the infrared (IR) active peaks of BrH2C–C≡CH(ads) such as vCH2 [2956 cm−1(s) and 3020 cm−1(as)], v≡CH (3320 cm−1) and vC≡C (2150 cm−1) gradually vanish as the rupture of C–Br bond occurs and then the resulting IR active peaks such as C=C=C (1812 cm−1), ω-CH2 (780 cm−1) and δ-CH (894 cm−1) appear due to the formation of H2C=C=CH(ads) which are in a good agreement with experimental reflection adsorption infrared spectrum (RAIRS) at temperatures of 110 and 200 K, respectively. Finally, the total energy profiles indicate that the reaction barriers for the scission of C–Br within BrH2C–C≡CH(ads) along both direct and indirect dissociation pathways are very close due to a similar rupture of C–Br bond leading to a similar transition state.