Exponent Analysis Research Articles

Conduction Mechanism and Dielectric Properties in Polyaniline/Titanium Dioxide Composites

The conductivity and dielectric properties of polyaniline (PANI) and PANI/TiO2 composites have been studied over a temperature range (313-393 K) and frequency range (25 Hz- 50 MHz). The nature of temperature and frequency-dependent conductivity can be explained by Jonscher’s universal power law and used to find the related parameters such as frequency exponent (s), dc conductivity (σdc), and crossover frequency (ωH). Besides, the frequency exponent analysis through a distinct model suggests that the conduction occurred through small polaron tunnelling in all compositions and at different temperatures. On the other hand, the enthalpy of migration (Hm), dissociation enthalpy of cation from its indigenous location alongside a compensating center (Hf), and the activation energy were also calculated using the Arrhenius relation. The temperature-dependent dc conductivity was examined in the framework of the theoretical model; Mott’s variable range hopping model (VRH) and experimental results were in good agreement with the 3-dimensional VRH model. As a function of temperature, dielectric constants (ε’ and ε”) increase while decreasing with an increasing dopant. Being such a high dielectric constant value, these composites can be used as frequency converters, modulators, and dielectric amplifiers.

Enhanced B-site cation ordering and improved magnetic properties: Role of A-site Bi substitution in La2NiMnO6 double perovskites

The influence of A-site Bi substitution on B-site cation ordering, crystal structure, and magnetic properties of La2-xBixNiMnO6 double perovskites has been studied. Compounds with and without Bi substitution adopt monoclinic crystal structure with the P21/n space group as determined from the Rietveld refinement of x-ray diffraction data. X-ray photoelectron spectroscopy indicated an increase in Mn4+/Mn3+ and Ni2+/Ni3+ ratios in Bi substituted sample, and hence emphasized improved Ni and Mn ordering. This has been well supported by bond length analysis which suggested a compression in<Mn–O>and elongation in<Ni–O>bond lengths. The Bi substituted compound showed a relatively larger moment as compared to the parent sample, which was attributed to the suppression of antisite disorder i.e., enhanced Ni-Mn cation ordering due to Bi substitution. The temperature-dependent dc magnetization studies reveal a ferromagnetic to paramagnetic phase transition, and the Curie temperature has been observed to increases with Bi substitution. The critical exponent analysis around phase transition also confirmed the increase in the critical temperature for the Bi substituted compound. A detailed analysis of the critical behavior has been performed using different models, which suggested a long-range ferromagnetic ordering for both pure and Bi substituted samples.

A new AG-AGB estimation model based on MODIS and SRTM data in Qinghai Province, China

The ability to rapidly and effectively monitor the alpine grassland aboveground biomass (AG-AGB) is very important to grassland resource management and sustainable use. The present satellite-driven models for estimating AG-AGB still need be improved due to incomplete monitoring indicators and a lack of sufficient in situ measurements. Thus, this study proposes a new AG-AGB estimation model based on MODIS and SRTM data for improving the estimation accuracy of AG-AGB. In this new model, first six estimation indicators were obtained from 33 spectral and environmental indicators using Extreme Gradient Boosting (XGBoost) and correlation analysis. Second, multiple linear regression (MLR), a back propagation neural network (BPNN), a support vector machine (SVM), and the random forest (RF) algorithm were used to build the new model based on the long-term AG-AGB field measurements and the corresponding to six estimating indicators for 6128 samples. Third, the new model was used to map the spatial distribution of the AG-AGB in Qinghai Province, China. And, the trends of the AG-AGB changes from 2005 to 2018 were analyzed using Sen and Mann-Kendall trend analysis and Hurst exponent analysis. The results show that 1) the six estimation indicators (EVI, radiation, altitude, B5/B7, latitude, and precipitation) were confirmed to be important for estimating the AG-AGB. 2) Among the MLR, BPNN, SVM, and RF, the model based on MODIS and SRTM data constructed using the RF algorithm was determined to be the best new model, with an R2 of 0.938 and a relative root mean square error (RRMSE) of 19.88%, for accurately estimating the AG-AGB based on 6128 samples. 3) Moreover, a set of 1200 samples was determined to be the lowest sample size for constructing the new model, with an R2 of nearly 0.923 and an RRMSE of 25%. 4) The AG-AGB can be more accurately mapped using the new model (R2 of 0.800 and RRMSE of 23.91%), indicating that the new model based on MODIS and SRTM data has the potential to map the AG-AGB over a large area. And over the last 14 years, the AG-AGB overall trends were that the areas with increasing AG-AGB (67.65%) were much larger than those with decreasing AG-AGB (32.35%), and the area of the sustainable AG-AGB increase accounts for 39.75% of the total alpine grassland area in northeastern and southwestern parts of Qinghai Province. The proposed estimation model based on MODIS and SRTM data is more suitable, greatly improves the accuracy of AG-AGB modeling, and provides a scientific basis for the reasonable use and management of alpine grassland resources.

Magnetostructural transition, magnetocaloric effect and critical exponent analysis in Nd(Co0.8Fe0.2)2 alloy

In the present study, the magnetostructural transition, magnetocaloric effect (MCE) and critical behavior of Nd(Co0.8Fe0.2)2 alloy in the vicinity of ferromagnetic transition were studied. The Rietveld refinement analysis of in-situ synchrotron XRD patterns reveals that the present alloy crystallizes in the cubic phase (space group Fd3̅m) at room temperature and undergoes a structural transition to the tetragonal phase (space group I41/amd) below its Curie temperature. Temperature dependent heat capacity measurements under different applied magnetic fields were carried out to investigate the magnetocaloric properties. The maximum magnetic entropy change (ΔSM) of 5.6 J/kgK and relative cooling power (RCP) of 445.7 J/kg are obtained over the wide working temperature range of 82 K under magnetic field change of 5 T. The corresponding adiabatic temperature change (ΔTad) is evaluated to be 2.9 K under the same magnetic field change. The sample shows metallic behavior with its significant magnetoresistance value of −41.2% around phase transition at magnetic field change of 5 T. These results may make Nd(Co0.8Fe0.2)2 alloy as one of the promising material for refrigeration applications. Moreover, the critical parameters (β, γ, and δ) were deduced independently from the magnetization data by using different techniques such as modified Arrott plot, Kouvel–Fisher method and critical magnetization isotherms. The derived β=0.319, γ =1.214, and δ=4.78 values are found to be consistent with the 3D-Ising class signifying the presence of short-range exchange interactions in this alloy. We have further shown that these exponents follow the M(H, ε)=|(T − TC)/TC |βf±H/|(T − TC)/TC|β+γ scaling equation very well for both the above and below Curie temperature demonstrating that the estimated exponents are unambiguous and are intrinsic to this alloy system.

Eastern and North Eastern sub-divisions of India : An analysis of trend and chaotic behaviour of rainfall in different seasons

The aim of the study is to understand trend or non-linearity along with a chaotic behaviour, if any, of Eastern and North Eastern sub-divisional rainfall, namely Orissa, Gangetic West Bengal, Sub Himalayan West Bengal, Assam and Meghalaya and also Nagaland, Manipur, Mizoram and Tripura based on rainfall data of 143 years (1871-2013). The analysis is performed for examining behaviour of rainfall in each of the seasons, namely, Pre monsoon, South West monsoon, North East monsoon and also Annual rainfall extracted from the monthly data. For that purpose, a trend analysis with Hurst Exponent and non-linearity analysis with Lyapunov Exponent are employed. The analysis revealed that rainfall of Orissa is persistent for all the seasons whilst the rainfall is persistent in Gangetic West Bengal in Pre monsoon and North East monsoon and Assam and Meghalaya along with Nagaland, Manipur, Mizoram and Tripura exhibit persistent behaviour in South West Monsoon and annually. Sub Himalayan West Bengal exhibit persistence in annual rainfall only. Chaotic tendency in low magnitude is located in many cases whilst non-chaotic situation has occurred when the persistence is found, mainly in pre-monsoon season. Moreover, the analysis of Hurst and Lyapunov Exponent revealed to identify two groups of sub-divisions with exactly similar region of every respect. Those two groups contain (i) sub-divisions Orissa and Assam and Meghalaya and also (ii) sub-divisions Sub Himalayan West Bengal and Nagaland, Mizoram, Manipur and Tripura although those are at distances of hundreds of kilometers away. The behaviour of those subdivisions in a group has similar behaviour in all respects.

Postural stability assessment in flatfoot subjects trough lyapunov exponent analysis

Abstract Background Flatfoot subjects presents an increase plantar foot area related to a plantar pressure feedback impairment. The Lyapunov Exponent is considered a nonlinear parameter used to characterize a signal chaotic behavior measuring the information rate loss from time series, i.e., for the Center of Pressure data. This exponent is used to quantify and measure the capability and resistance of subjects to several perturbations. The study purpose was to investigate the postural stability differences among foot posture condition through the Lyapunov Exponent analysis. Methods The sample of the observation descriptive study consisted of 31 participants (23.26 yo ± 4.43 SD) comprising a total of 62 feet, where 15 integrated into the experimental group with bilateral flatfoot condition and the remaining 16 in the control group with the bilateral neutral foot condition. Subjects were screened, before posture analysis, using the Navicular Drop Test and Resting Calcaneal Stance Position test, to characterize each group. All participants were subjected to a bipedal weight-bearing stance posture stability analysis a force platform, both in eyes-open and closed condition. The Lyapunov Exponent was calculated using the Matlab-R2020b (MathWorks Inc., USA) software. Results Regarding the Center of Pressure outcomes, only the Lyapunov Exponent value upon the Antero-Posterior component regarding groups in the eyes closed condition (diff = 3.09°, p = .016) presented a significant result. Conclusion Flatfoot subjects present a significant difference compared to neutral foot participants, in bipedal weight-bearing stance, in the eyes closed condition regarding the Lyapunov Exponent. This relates to increase variability and decrease stability regarding the Antero-Posterior component.

Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data

The Qinling-Daba Mountains region (Qinba) is an important geographical transitional zone across the north and south of China. To comprehensively understand the ecological transition in the Qinba over past two decades, this study assessed and predicted the spatiotemporal variations of vegetation comparatively, using 250-m time series MODIS NDVI and EVI products. From 2000 to 2019, remarkable increases were observed both in annual and seasonal NDVI and EVI (P < 0.05), and the increasing rate of NDVI was higher than that of EVI for all temporal scales, except summer. Compared to NDVI, larger areas of no significant change were obtained in annual, autumn, and winter EVI, primarily distributed in the high-altitude regions of western Qinba. All assessed vegetation types increased significantly during past two decades except alpine vegetation and marsh, however, the seasons in which the significant increase in NDVI and EVI occurred varied for different vegetation types. Hurst exponent analysis suggested that inconsistent characteristics of vegetation dynamic trends were stronger in the future across Qinba. The area proportion of unfavorable trends, identified mainly covered with cultivated vegetation and scrub, was much larger than that of favorable trends, especially detected by EVI. Areas likely to experience vegetation variation of unfavorable and undetermined trends deserve high focus.

Mode Crystallography Analysis through the Structural Phase Transition and Magnetic Critical Behavior of the Lacunar Spinel GaMo4Se8.

In the lacunar spinels, with the formula AB4X8, transition-metal ions form tightly bound B4 clusters resulting in exotic physical properties such as the stabilization of Néel-type skyrmion lattices, which hold great promise for energy-efficient switching devices. These properties are governed by the symmetry of these compounds with distortion of the parent noncentrosymmetric F4̅3m space group to the polar R3m, with recent observation of a coexisting Imm2 low-temperature phase. In this study, through powder neutron diffraction, we further confirm that a metastable Imm2 coexists with the R3m phase in GaMo4Se8 and we present its structure. By applying the mode crystallography approach to the distortions together with anisotropic microstrain broadening analysis, we postulate that the formation origin of the minority Imm2 phase stems from the high compressive stress observed in the R3m phase. Bond valence sum analysis also suggests a change in electronic configuration in the transition to Imm2 which could have implications on the electrical properties of the compound. We further establish the nature of the magnetic phase transition using critical exponent analysis obtained from single-crystal magnetization measurements which shows a mixture of tricritical mean-field and 3D Heisenberg behavior [β = 0.22(4), γ = 1.19(1), and δ = 6.42(1)]. Magnetoentropic mapping performed on a single crystal reveals the signature of a positive entropy region near the magnetic phase transition which corresponds to the skyrmion phase field observed in a polycrystalline sample.

Chaotic dynamics of ionospheric clutter from high frequency surface wave radar

The detection performance of high frequency surface wave radar is impacted by the ionospheric clutter. In order to suppress the ionospheric clutter, it is important to research the physical mechanism of ionosphere. In this paper, it is demonstrated, convincingly for the first time, the chaotic dynamics of the ionospheric clutter. The results are obtained using the actual data collected from a series of experiments in Weihai Radar Station, China. The study includes the stationarity and nonlinearity test, phase-space reconstruction, correlation dimension analysis and Lyapunov exponents analysis. And finally, it is found that ionospheric clutter in HFSWR is a chaotic dynamical process with relatively few degrees of freedom, in contrast to the stochastic approach where the ionospheric clutter is viewed as a random process with a large number of degrees of freedom[1]. Also, this new approach will further provide an original way to analyse the ionospheric clutter suppression method.

Drought monitoring in arid and semi-arid region based on multi-satellite datasets in northwest, China.

Drought is a complex natural disaster affected by multiple climate factors and underlying surface. In recent years, drought monitoring indices of remote sensing have been widely applied to monitor drought in a certain region or global. However, some remote sensing drought monitoring indices do not consider the drought-causing factors enough to reflect the comprehensive drought situation of a region fully. In this paper, a new remote sensing drought monitoring index, called Remote Sensing Drought Evaluation Index (RSDEI), was constructed by combining Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), and Soil Moisture Condition Index (SMCI) using the spatial principal component analysis (SPCA) method. The reasonableness of RSDEI was test and verified using Net Primary Productivity (NPP), Standardized Precipitation Evapotranspiration Index (SPEI), and unit area crop yield. The RSDEI was also applied to the drought condition monitoring of the northwest arid and semi-arid region from 2001 to 2019.The result demonstrated that the results showed that the RSDEI had a high correlation coefficient with SPEI-12 (R=0.85, p<0.01). It is concluded that the correlation coefficient between RSDEI and NPP is 0.74 at 95% confidence level, which indicates that RSDEI and NPP have a strong correlation. Then, the correlation between RSDEI and crop yield per unit area is 0.89. The results of RSDEI showed that the drought in northwest China started in May and lasted in September from 2001 to 2019. The lowest value of RSDEI appeared in May, which inflected the significant difference of drought level in different month in northwest China. The result of CV (coefficient of variation) showed that the drought variation in the study area had a stable low fluctuation condition as a whole, in the northwest and northeast of study area, which indicated that the changes of drought were different in the past 19 years. The Hurst exponent analysis showed that the area with the positive evolution of Hurst index (0.5<H<1) is 1,845,046.669 km2,which accounts for 75.9% of the total area, while the area with reverse evolution characteristics (H<0.5) accounts for 24.1% of the total area. The result obtained above reflected that the drought changes in most regions are better than that in the past 19 years. The trend gradually changes from drought to humid.

Quantum signatures of chaos in a cavity-QED-based stimulated Raman adiabatic passage

Nonlinear stimulated Raman adiabatic passage (STIRAP) is a fascinating physical process that dynamically explores chaotic and nonchaotic phases. In a recent paper [A. Dey and M. Kulkarni, Phys. Rev. Res. 2, 042004(R) (2020)], such a phenomenon is realized in a cavity-QED platform. There, the emergence of chaos and its impact on STIRAP efficiency are mainly demonstrated in the semiclassical limit. In the present paper we treat the problem in a fully quantum many-body framework. With the aim of extracting quantum signatures of a classically chaotic system, it is shown that an out-of-time-ordered correlator (OTOC) measure precisely captures chaotic and nonchaotic features of the system. The prediction by OTOC is in precise matching with classical chaos quantified by Lyapunov exponent analysis. Furthermore, it is shown that the quantum route corresponding to the semiclassical followed state encounters a dip in single-particle purity within the chaotic phase, depicting a consequence of chaos. A dynamics through the chaotic phase is associated with spreading of the many-body quantum state and an irreversible increase in the number of participating adiabatic eigenstates.

Long-term dependency between sovereign bonds and sectoral indices of India: evidence using Hurst exponent and wavelet analysis

PurposeThe purpose of this study is to provide a new way to optimize a portfolio and to show that combining the Hurst exponent and wavelet analysis may help to increase portfolio returns.Design/methodology/approachThe authors use the Hurst exponent and wavelet analysis to study the long-term dependencies between sovereign bonds and sectoral indices of India. The authors further construct and evaluate the performance of three portfolios constructed on the basis of Hurst standard deviation (SD) – global minimum variance (GMV), most diversified portfolio (MDP) and equal risk contribution (ERC).FindingsThe authors find that an ERC portfolio generates positive superior return as compared other two. Since our sample includes periods of two crisis – post-2007 financial crisis and the ongoing pandemic, this study reveals that combining government bond with equities and gold provides a higher returns when the portfolios are constructed using the risk exposures of each asset in the overall portfolio risk.Practical implicationsThe findings provide guidance to portfolio managers by helping them to select assets using the Hurst approach and wavelet analysis thereby increasing the portfolio returns.Originality/valueIn this study, the authors use a combination of Hurst exponent and wavelet analysis to understand the long-term dependencies among various assets and provide a new methodology to optimize a portfolio. As far as the authors’ knowledge, no study in the past has attempted to provide a joint framework for portfolio optimization and therefore this study is the first to apply this methodology.

A flexible image encryption algorithm based on 3D CTBCS and DNA computing

In this paper, a novel image encryption algorithm based on a new discrete chaotic system is presented. The chaotic characteristics of the new chaotic system are analyzed by phase diagram, Lyapunov exponent, bifurcation diagram and complexity analysis. The randomness of chaotic sequences is test by the NIST SP 800-22 test package. The calculation time with different length sequence is recorded. Based on the analyses results, a flexible encryption scheme is designed for 2D image and 3D image. The hash value which is calculated from the SHA-256 hash function is used to change the initial conditions of the discrete system firstly. And then, the chaotic sequences are used to scramble and spread the value for the images by Arnold matrix and DNA diffusion algorithm. The security analysis shows that the proposed encryption algorithm possesses higher security features to preserve the subject and resist conventional attack. The results of this paper offer a different realization scheme for protection of image files.

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV &MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people.Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y–z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y)=100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y–u), exposed-removed (y–v), exposed-virus in the reservoir (y–w), asymptomatically infected-removed (z–v), symptomatically infected-removed (u–v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z–w), at asymptomatically infected (z)=10, the value of viruses in the reservoir (w)=50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Accelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to -understand these events’ propagation in the near future. We have considered sixteen extreme climate indices defined by the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices from a long-term dataset (1951–2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non-stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain combinations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indicating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16-year bands, and the two-year period was dominant in the global power spectrum around 1970–1990. One interesting finding is that a dominant two-year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes.

A 3D chaotic system with piece-wise lines shape non-hyperbolic equilibria and its predefined-time control

In this paper, a novel 3D chaotic system with an infinite number of equilibria is proposed and its predefined-time control is studied. The system has non-hyperbolic equilibria with piece-wise shape, which is special and rarely mentioned before. Through 0-1 test, Lyapunov exponent, bifurcation diagram and complexity analysis, the system is deeply investigated. A reverse bubble (Feigenbaum remerging tree) is found in the system, which proves the anti-monotonicity. Furthermore, the circuit of the system is designed and the real experiment is carried out to verify its dynamic characteristics. Finally, according to the theory of predefined-time stability, a predefined time controller is designed for the system. By adding only one controller to the system, the objective of stabilizing the system within a predefined time can be achieved successfully, and simulation analysis shows good performance of the controller.

128 Nonlinear Dynamic Analysis of Heart Rate Variability Can Detect Sepsis in Severe Burns

Abstract Introduction Sepsis after burn injury affects 50–84% of adult patients and ~55% of pediatric patients. The diagnosis of sepsis in patients with severe burns is complicated by the overlap of clinical signs of post-burn hypermetabolic response with those of sepsis. Earlier detection of sepsis could lead to improved outcomes and decreased morbidity and mortality. Nonlinear dynamic (ND) analysis of heart rate variability (HRV) can identify non-random relationships between different HRV parameters by using a complex oscillogram. ND can be obtained even when heart rate recordings are weak or poor in quality, which is common in the setting of burns. We hypothesized that ND could detect differences in HRV after severe burns. Methods All patients enrolled during the study period at our institution with &amp;gt;20% total body surface area (TBSA) burn were eligible for this study. High-definition heart rate monitoring was collected three times for each patient in five-minute intervals. The first collection occurred after patient ICU admission, and the third collection occurred 16–24 hours. Matlab was used to analyze heart rate using the following common ND methods: Correlation dimension (CD), detrended fluctuation (DF), largest Lyapunov exponent (LLE), approximate entropy (AE), fractal dimension (FD), Hurst exponent (HE), and recurrence quantification analysis (RQA). Additionally, several linear time-domain parameters were analyzed, including heart rate (R), RR interval (RR), SD1, and SD2. Results A total of six non-septic (NS) and four septic (S) patients were enrolled in the study. There were no significant differences between the two groups in age, sex, percent TBSA, or percent TBSA of third-degree burns. Two nonlinear parameters were significantly different between NS and S patients: AE and the density of recurrence points in RQA. AE was significantly lower in NS than S patients at all time points [(0.931±0.011 vs. 0.960±0.024, p=0.001); (0.923±0.019 vs. 0.959±0.025, p=0.008); (0.933±0.011 vs. 0.964±0.025, p=0.013)]. The density of recurrence points in RQA were significantly higher than in NS than S patients at all time points. [(90.4±40.2 vs. 41.4± 27.8, p=0.029), (102.2±39.4 vs. 40.1±22.9, p=0.008), (92.5±34.4 vs. 45.5±33.9, p=0.043)]. Conclusions The two ND parameters AE and RQA, can be used to distinguish between patients with and without sepsis in the setting of severe burns.

A New Memristive Chaotic System with a Plane and Two Lines of Equilibria

A new 4D memristive chaotic system with an infinite number of equilibria is proposed via exhaustive computer search. Interestingly, such a new memristive system has a plane of equilibria and two other lines of equilibria. Lyapunov exponent and bifurcation analysis show that this system has chaotic solutions with coexisting attractors. The basins of attraction of the coexisting attractors show chaos, stable fixed-points, and unbounded solutions. Furthermore, the 2D parameter space of the system is explored to find the optimum values of the parameters using the ALO (Ant Lion Optimizer) optimization algorithm.

Modes decomposition forecasting approach for ultra-short-term wind speed

The accurate forecasting of ultra-short-term wind speed is of great significance in theory and practice. This paper proposes a modes decomposition forecasting approach based on adaptive variational mode decomposition and weighted combination models for ultra-short-term wind speed. First, an adaptive variational mode decomposition algorithm is used for decomposing the original ultra-short-term wind speed time series into several modal components. Second, auto regressive integrated moving average, support vector machine and the improved long short-term memory are determined as forecasting models of different components by Hurst exponent analysis. Then, an improved particle swarm optimization algorithm is proposed to optimize the weight coefficient of each forecasting model. Finally, the final forecasted value is obtained by multiplying the forecasted value of each sub-forecasting model by their respective weight coefficient. Four groups of measured ultra-short-term wind speed data with 5-minute, 10-minute, 20-minute and 30-minute sampling periods are taken as the research object. Compared with other single or combination forecasting models, the proposed forecasting approach has higher prediction accuracy and more promising prediction performance for ultra-short-term wind speed.

Vegetation Productivity Dynamics in Response to Climate Change and Human Activities under Different Topography and Land Cover in Northeast China

Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem.