Breaking down walls: Continuous potential models for internal motions in NMR spin relaxation.

Simple physical models for restricted diffusion in a potential, which provide important insights for NMR spin relaxation, usually are based on free diffusion within rigid boundaries or diffusion in relatively simple continuous potential energy surfaces. The diffusion-in-a-cone model is an example of the former and diffusion in an N-fold cosine potential is an example of the latter. The present work models restricted diffusion for arbitrary potential energy functions on the surface of a cone or a sphere, by expanding the potentials in Fourier or spherical harmonic series, respectively. The results exhibit simple relationships between generalized order parameters and effective correlation times, critical for analysis of experimental spin relaxation data, and illustrate the transition from diffusive-like to jump-like behavior in multi-well potentials.

Glass Transition Temperatures and Thermal Conductivities of Polybutadiene Crosslinked with Randomly Distributed Sulfur Chains Using Molecular Dynamic Simulation.

The thermal conductivities and glass transition temperatures of polybutadiene crosslinked with randomly distributed sulfur chains having different lengths from mono-sulfur (S1) to octa-sulfur (S8) were investigated. The thermal conductivities of the related models as a function of the heat flux autocorrelation function, applying an equilibrium molecular dynamic (EMD) simulation and the Green-Kubo method, were studied for a wide range of temperatures. The influence of the length of sulfur chains, degree of crosslinking, and molar mass of the crosslinker on the glass transition temperature and final values of thermal conductivities were studied. First, the degree of crosslinking is considered constant for the eight simulation models, from mono-sulfur (S1) to octa-sulfur (S8), while the molar mass of the sulfur is increases. The results show that the thermal conductivities of the crosslinked structure decrease with increasing temperature for each model. Moreover, by increasing the lengths of the sulfur chains and the molar weight of the crosslinker, thermal conductivity increases at a constant temperature. The MD simulation demonstrates that the glass transition temperature and density of the crosslinked structure enhance as the length of the sulfur chains and molar mass of the sulfur increase. Second, the molar weight of sulfur is considered constant in these eight models; therefore, the degree of crosslinking decreases with the increase in the lengths of the sulfur chains. The results show that the thermal conductivities of the crosslinked structure decrease with the increase in the temperature for each model. Moreover, by increasing the lengths of sulfur chains and thus decreasing the degree of crosslinking, the trend in changes in thermal conductivities are almost the same for all of these models, so thermal conductivity is constant for a specific temperature. In addition, the glass transition temperature and density of the crosslinked structure decrease.

Modified Nested Barker Codes for Ultra-Wideband Signal-Code Constructions.

Currently, various applications of ultra-wideband signal-code constructions are among the most vibrant technologies, being implemented in very different fields. The purpose of this research consists of analyzing Barker codes and searching for the optimal nested representations of them. We also aim to synthesize signal-code constructions based on the tenets of nesting of alternative modified Barker codes, which employ an asymmetric alphabet. The scientific merit of the paper is as follows: on the basis of new analytic expressions, modified nested codes and signal-code constructions were obtained, applicable for the establishment of the unambiguous association of the component values of the nested codes with any lobes of the normalized autocorrelation function. With these analytical expressions, we are, hence, able to determine the values of the binary asymmetrical components of the nested codes related to the side lobes of the normalized autocorrelation function. In this way, we clearly obtain better (low) levels for these lobes than by using the autocorrelation function, as established by the equivalent conventional Barker codes, including the nested constructions. Practical application of these modulated ultra-wideband signals ensures improved correlational features, high-fidelity probabilistic detection, and more precise positional detection of physical bodies depending on the range coordinate.

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

Improved Adaptive Multipoint Optimal Minimum Entropy Deconvolution and Application on Bearing Fault Detection in Random Impulsive Noise Environments.

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise.

Wide-field Intensity Fluctuation Imaging.

The temporal intensity fluctuations contain important information about the light source and light-medium interaction and are typically characterized by the intensity autocorrelation function, g2(τ). The measurement of g2(τ) is a central topic in many optical sensing applications, ranging from stellar intensity interferometer in astrophysics, to fluorescence correlation spectroscopy in biomedical sciences and blood flow measurement with dynamic light scattering. Currently, g2(τ) at a single point is readily accessible through high-frequency sampling of the intensity signal. However, two-dimensional wide-field measurement of g2(τ) is still limited by camera frame rates. We propose and demonstrate a 2-pulse within-exposure modulation approach to break through the camera frame rate limit and obtain the quasi g2(τ) map in wide field with cameras of only ordinary frame rates.

Thermal Conductivities of Uniform and Random Sulfur Crosslinking in Polybutadiene by Molecular Dynamic Simulation.

Thermal conductivities of polybutadiene crosslinked with sulfur as a function of the heat flux autocorrelation function by using an equilibrium molecular dynamic (EMD) simulation were investigated. The Green-Kubo method was used to calculate thermal conductivities. All simulations were performed by applying the LAMMPS software (version 3 Mar 2020) package. The united-atom force field (OPLS-UA) from the Moltemplate software (version 2.20.3) was applied in the simulations. The influence of uniform and random distributions of sulfur in polybutadiene on the final value of thermal conductivities was studied by polymeric model structures with similar and variable degrees of crosslinking. The results showed that for identical degrees of crosslinking, the distribution of crosslinkers in the polymeric model structures significantly influenced the final value of thermal conductivity. Moreover, the influence of the crosslinking degree on the final value of thermal conductivity was studied by considering polymeric model structures with different degrees of crosslinking. The results demonstrate that by having a random distribution of sulfur, the thermal conductivity will be enhanced. However, by increasing the degree of crosslinking to the higher percentage in random crosslinked model structures, the value of thermal conductivity drops significantly due to possible higher crystallization of the model structures, which decrease the degree of freedom for phonon contributions.

Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel.

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework.

A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal.

The signal measured by the maglev gyro sensor is sensitive to the influence of the instantaneous disturbance torque caused by the instantaneous strong wind or the ground vibration, which reduced the north-seeking accuracy of the instrument. To address this issue, we proposed a novel method combining the heuristic segmentation algorithm (HSA) and the two-sample Kolmogorov-Smirnov (KS) test (named HSA-KS method) to process the gyro signals and improve the north-seeking accuracy of the gyro. There were two key steps in the HSA-KS method: (i) all the potential change points were automatically and accurately detected by HSA, and (ii) the jumps in the signal caused by the instantaneous disturbance torque were quickly located and eliminated by the two-sample KS test. The effectiveness of our method was verified through a field experiment on a high-precision global positioning system (GPS) baseline at the 5th sub-tunnel of the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi Province, China. Our results from the autocorrelograms indicated that the jumps in the gyro signals can be automatically and accurately eliminated by the HSA-KS method. After processing, the absolute difference between the gyro and high-precision GPS north azimuths was enhanced by 53.5%, which was superior to the optimized wavelet transform and the optimized Hilbert-Huang transform.

Physiological evaluations of low-level impulsive sounds generated by an air conditioner.

Air conditioners are typically installed in buildings and vehicles to control thermal conditions for long periods of time. Air conditioners generate certain types of sounds while functioning, which are among the main noise sources in buildings and vehicles. Most sounds produced by the air conditioner do not change with time, and the sound quality of steady sounds has been investigated. However, air conditioners can generate low-level impulsive sounds. Customers complain of the discomfort caused when these sounds disturb the silence in their living rooms and bedrooms. This study aimed to determine the physical factors that have a significant effect on physiological responses to low-level impulsive sounds produced by air conditioners. We used physiological responses because it is difficult for people to evaluate sounds psychologically when they are sleeping or are not focused on the sounds. The A-weighted equivalent continuous sound pressure level (LAeq) and the factors extracted from the autocorrelation function (ACF) were evaluated as physical factors. Participant responses on electroencephalography (EEG) were evaluated. The correlation between the EEG responses and ACF factors was determined. The LAeq, peak, and delay time to the first maximum peak of the ACF were identified as significant factors for physiological responses to low-level impulsive sounds.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Partial Autocorrelation Diagnostics for Count Time Series.

In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule-Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.

Driving effect of multiplex factors on human brucellosis in high incidence region, implication for brucellosis based on one health concept.

Brucellosis is a typical zoonosis driven by various risk factors, including environmental ones. The present study aimed to explore the driving effect of environmental factors on human brucellosis in a high incidence rate area, which provides understanding and implications in mitigating disease transmission risk in a multi-system between the human-animal-environment interface for preventing and controlling brucellosis based on the One Health concept. Based on the monthly time series data of human brucellosis and environmental variables, a Seasonal Autoregressive Integrated Moving Average Model with explanatory variables (SARIMAX) was applied to assess the association between environmental indicators and human brucellosis incidence (IHB). The results indicated distinct seasonal fluctuation during the study duration, tending to climb from April to August. Atmospheric pressure, precipitation, relative humidity, mean temperature, sunshine duration, and normalized difference vegetation index significantly drive IHB. Moreover, the well-fitting and predicting capability were performed and assessed in the optimal model was the SARIMAX (0,1,1) (0,1,1)12 model with the normalized difference vegetation index (ß = 0.349, P = 0.036) and mean temperature (ß = 0.133, P = 0.046) lagged in 6 months, and the precipitation lagged in 1 month (ß = -0.090, P = 0.004). Our study suggests the association between environmental risk factors and human brucellosis infection, which can be contributed to mitigating the transmission risk in the environmental drivers in a multi-system interface through comprehensive prevention and intervention strategies based on the One Health concept.

Molecular Dynamics Study of Structural and Transport Properties of Silver Iodide Using Effective Charges.

The superionic conductor, solid state, and body-centered cubic structure, silver iodide at room temperature, has been studied via molecular dynamics simulations. The calculated results using pairwise Coulomb-Buckingham potential, zero pressure on the sample, a semi-rigid model system of 1000 Ag and 1000 I ions, (NVE) as a statistical ensemble, and an effective charge of Z=0.63 for the pairs Ag-Ag and I-I, were found to be consistent with experimental data and one study using Z=0.60, different potential, and simulation software. For the pair Ag-I, there is a discrepancy due to the high silver ion diffusion. The calculated value of the diffusion constant of the silver ion is greater than iodide ion. The dynamic transport properties (mean square displacement, velocity autocorrelation function) results indicated typical behavior reported by other authors, using different potentials in their DM simulations for iodine and silver ions.

Analysis of sea level fluctuations around the Australian coast with anomaly time series analysis approach.

Thirty two-year observations (1990-2022) of water level from a total of 14 high-quality acoustic tide stations around the coast of Australia deployed for the Australian Baseline Sea Level Monitoring Project were analyzed. The following three approaches were used: Fourier Transform (FT), Empirical Mode Decomposition (EMD), and Singular Spectrum Analysis (SSA). The water level anomaly was observed to have predominant annual variations with a period of about 12 months based on the Fourier transform. The intrinsic components of stations were extracted in the EMD analysis and the mean period of each of the components was calculated using the zero down crossing method. A regular association was observed between the order of modes and the mean period such that the periods increase by a factor of two on successive modes. The third method used for anomaly analysis was SSA. The number of the obtained components in this method was less than in the EMD. Moreover, the order observed for the components' period in the EMD was not seen in this method. Spectral analysis of Autocorrelation function (ACF) has demonstrated that peak frequencies are almost the same with anomaly spectra so the dominant modes in anomalies are also present in the ACF.

Modeling Long-Range Dynamic Correlations of Words in Written Texts with Hawkes Processes.

It has been clarified that words in written texts are classified into two groups called Type-I and Type-II words. The Type-I words are words that exhibit long-range dynamic correlations in written texts while the Type-II words do not show any type of dynamic correlations. Although the stochastic process of yielding Type-II words has been clarified to be a superposition of Poisson point processes with various intensities, there is no definitive model for Type-I words. In this study, we introduce a Hawkes process, which is known as a kind of self-exciting point process, as a candidate for the stochastic process that governs yielding Type-I words; i.e., the purpose of this study is to establish that the Hawkes process is useful to model occurrence patterns of Type-I words in real written texts. The relation between the Hawkes process and an existing model for Type-I words, in which hierarchical structures of written texts are considered to play a central role in yielding dynamic correlations, will also be discussed.

Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar.

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and lower linearity requirements (e.g., compared to orthogonal-frequency division multiplexing radars), as well as its high velocity unambiguity and multiple-input multiple-output operation capability, all of which are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.

Morphologic characterization and fractal analysis of lapped and polished surfaces of quartz single crystals.

Lapping and polishing are industrial processes sometimes used alternatively for surface finishing of hard and brittle materials. This article presents advanced image analysis of surfaces of quartz crystal blanks finished by lapping and polishing. Scanning electron micrographs were obtained from workpiece surfaces parallel to Y-, AT-, and Z-cut crystal planes treated with different normal stress and abrasive grit size, and stereometric and fractal/multifractal approaches were used to analyze the respective surfaces. Fractal dimensions and segmentation parameters were able to decode the effect of normal stress increasing on the surface roughness of lapped and polished samples. Moreover, the texture isotropy and the bifractal-hence agglomerated-nature of the surface patterns, suggest that both treatments dismiss the anisotropic signature of hardness and fracture toughness inherent to each crystal plane. This study provides promising results regarding the applicability of fractal analysis in the assessment of surfaces severely worn by the combined effect of brittle microcracking and plastic deformation mechanisms.

State of the art in total pulse production in major states of India using ARIMA techniques.

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Time-Delay Identification Using Multiscale Ordinal Quantifiers.

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.